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• Satellite image (Landsat 8) used to estimate land surface temperature in a Mountainous region. • Single Channel JM&S and Split Window algorithms compared with observed temperatures. • Split Window algorithm depicted lower MAE and closer agreement with observations. Studying Land Surface Temperature (LST) provides various scientific, environmental, and practical advantages. It aids climate monitoring, urban heat island studies, environmental assessments, agriculture, urban planning, infrastructure development, and remote sensing. Algorithm selection depends on research constraints and goals, considering different algorithm strengths and limitations. In this study, the LST around the Sabalan volcanic peak was estimated using Landsat 8 satellite images captured by the OLI and TIRS, employing the Single Channel JM&S and Split Window algorithms. The obtained surface temperatures from both algorithms were converted to air temperature. Subsequently, the pixel air temperatures were compared to the corresponding synoptic station air temperatures of the studied region, considering the satellite's passage time. This comparison was executed using regression analysis (R2 and correlation coefficients) and statistical indices such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to validate the data. The results indicated R2 values and correlation coefficients of 0.04 and 0.97, respectively, for the Single Channel algorithm, and 0.12 and 0.69 for the SW algorithm, signifying a positive relationship and close alignment between these datasets. Furthermore, the examination of the results revealed root mean square error and mean absolute error with minimum errors of 4.20, 3.88, and 8.14, 8.97 °C in the SW method. By assessing the difference between mean LST and the synoptic station air temperatures, the SW approach exhibited lower temperature discrepancies across all stations and lower estimation errors compared to the Single Channel method, thus displaying higher accuracy and closer conformity to the actual temperature. [ABSTRACT FROM AUTHOR]

Surface Air Temperature (SAT) predictions, typically generated by Global Climate Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission scenarios. Machine Learning (ML) techniques can be employed to forecast long-term temperature variations, although this is a challenging endeavour with few drawbacks, such as the influence of scenarios involving greenhouse gas emissions. Therefore, the present study utilized multiple ML approaches such as Artificial Neural Networks (ANN), multiple linear regression, support vector machine and random forest, along with various daily predicted results of GCMs from Coupled Model Intercomparison Project Phase 6 as predictors and the "India Meteorological Department's" Maximum SAT (MSAT) as the predictand, to predict daily MSAT in the months of March, April and May (MAM) over Andhra Pradesh (AP) for the period 1981–2022. The results show that ANN outperforms other ML techniques in predicting daily MSAT, with a root mean square error of 1.41, an index of agreement of 0.89 and a correlation coefficient of 0.81. The spatial distribution of hot and heat wave days indicates that the Multiple Model Mean (MMM) underestimates these occurrences, with a minimum bias of 9 and 6 days, respectively. In contrast, the ANN model exhibits much smaller biases, with a maximum underestimation of 3 hot and 2 heat wave days. These findings demonstrate that MMM does not capture the maximum temperatures well, resulting in poor predictability. Further, future temperature projections were analysed from 2023 to 2050, which display a gradual increase in mean MSAT during MAM over AP. This research demonstrates the potential of ML techniques to enhance temperature forecasting accuracy, offering valuable insights for climate modeling and adaptation. The results are crucial for stakeholders in agriculture, health, energy, water resources, socio-economic planning, and urban development, aiding in informed decision-making and improving resilience to climate change impacts. [ABSTRACT FROM AUTHOR]

The adequate representation of interactions between the land surface and the atmosphere is of crucial importance in modern numerical weather prediction (NWP) systems. In this context, this study examines how errors in the planetary boundary layer (PBL) depend on the quality of near-surface prediction over land for medium-range NWP. Two series of 10-day forecasts from Environment and Climate Change Canada (ECCC)'s global deterministic prediction system were evaluated: one similar to what is currently used in ECCC's operational systems and the other with improved land surface modeling and land data assimilation. An objective evaluation was performed for the 2019 summer season in North America, with a special emphasis on three specific areas: northern Canada, the central US, and the southeastern US. The results indicate that the impact of the new land surface package is more difficult to interpret in the PBL than it is at the screen level. The error differences between the two experiments are quite distinct for the three regions examined. As expected, random errors (standard deviations) for air temperature and specific humidity in the PBL are directly linked with their own random errors at the screen level, with correlation coefficients decreasing from a value of one at the surface to values of about 0.2–0.3 a few kilometers above the surface. Less expected, however, is the fact that random errors in the lower atmosphere also strongly depend on changes in air temperature biases at the surface. Warmer near-surface conditions lead to increased random errors for air temperature in the lower atmosphere, in association with the development of the deeper PBL, with greater spatial variability. This finding is of particular interest when evaluating new configurations of NWP systems for implementation in national meteorological and environmental prediction centers. [ABSTRACT FROM AUTHOR]

Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951–31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance. [ABSTRACT FROM AUTHOR]

Agricultural sustainability is dependent on the ability to predict crop yield, which is vital for farmers, consumers, and researchers. Most of the works used the amount of rainfall, average monthly temperature, relative humidity, etc. as inputs. In this paper, an attempt was made to predict the yield of the citrus crop (Washington Navel orange, Valencia orange, Murcott mandarin, Fremont mandarin, and Bearss Seedless lime) using weather factors and the accumulated heat units. These variables were used as input parameters in an artificial neural network (ANN) model. The necessary information was gathered during the growing seasons between 2010/2011 and 2021/2022 under Egyptian conditions. Weather factors were daily precipitation, yearly average air temperature, and yearly average of air relative humidity. A base air temperature of 13.0 °C was used to determine the accumulated heat units. The heat use efficiency (HUE) for cultivars was determined. The Bearss Seedless lime had the lowest HUE of 9.5 kg/ha °C day, while the Washington Navel orange had the highest HUE of 20.2 kg/ha °C day. The predictive performance of the ANN model with a structure of 9-20-1 with the backpropagation was evaluated using standard statistical measures. The actual and estimated yields from the ANN model were compared using a testing dataset, resulting in a value of RMSE, MAE, and MAPE of 2.80 t/ha, 2.58 t/ha, and 5.41%, respectively. The performance of the ANN model in the training phase was compared to multiple linear regression (MLR) models using values of R2; for MLR models for all cultivars, R2 ranged between 0.151 and 0.844, while the R2 value for the ANN was 0.87. Moreover, the ANN model gave the best performance criteria for evaluation of citrus yield prediction with a high R2, low root mean squared error, and low mean absolute error compared to the performance criteria of data mining algorithms such as K-nearest neighbor (KNN), KStar, and support vector regression. These encouraging outcomes show how the current ANN model can be used to estimate fruit yields, including citrus fruits and other types of fruit. The novelty of the proposed ANN model lies in the combination of weather parameters and accumulated heat units for accurate citrus yield prediction, specifically tailored for Egyptian regional citrus crops. Furthermore, especially in low- to middle-income countries such as Egypt, the findings of this study can greatly enhance the reliance on statistics when making decisions regarding agriculture and climate change. The citrus industry can benefit greatly from these discoveries, which can help with optimization, harvest planning, and postharvest logistics. We recommended furthering proving the robustness and generalization ability of the results in this study by adding more data points. [ABSTRACT FROM AUTHOR]

Here we present a new eastern Baltic Chironomidae training set (TS) containing 35 sites that was collected and merged with neighbouring published Finnish (82 lakes) and northern part of the Polish (nine lakes) TSs. Chironomidae, non‐biting midges, are known to be strongly responsive to the July air temperature and are widely used to infer palaeotemperature. Several modern analogue‐based TSs necessary for calibrating the relationships between mean July air temperature (MJAT) and chironomids are available for Europe. However, none of these is representative of the transitional climate typical for eastern Baltic (Estonia, Latvia, Lithuania). The Finno–Baltic–Polish TS contains 121 sites and covers a geographically continuous 70–50°N latitudinal and 7 °C (12.1–19.2 °C) MJAT gradient. Canonical correspondence analysis revealed that, among the tested environmental variables (pH, water depth, dissolved oxygen, MJAT), the MJAT explains the highest amount of variation, both for the eastern Baltic separately and the Finno–Baltic–Polish TSs. The weighted averaging–partial least squares‐based cross‐validation test reveals that the Finno–Baltic–Polish TS has a low root mean square error of prediction (0.7 °C) confirming the high reliability of the TS. The temperature optima of the taxa included in the new Finno–Baltic–Polish TS and widely used Swiss–Norwegian TS were examined. The observed dissimilarities can be attributed to the differences in the temperature ranges represented by the TS, the taxonomic identification level, the general cosmopolitan taxa distribution patterns and the influence of TS‐specific geographic position, climatic or environmental conditions. The new Finno–Baltic–Polish TS adds to the knowledge on the modern distribution of Chironomidae taxa and widens the geographical area of reliable Chironomid‐based MJAT reconstructions into the eastern European lowland. [ABSTRACT FROM AUTHOR]

Measuring and modelling manure temperatures are crucial for estimating greenhouse gas emissions from liquid manure storage. The manure temperature was recorded at various depths in two swine slurry storage tanks situated in Vallentuna (VA) and Örsundsbro (OR) in Sweden. These data were used to assess the effectiveness of a revised mechanistic model for estimating manure temperatures, which incorporates the effects of wall shading, snow cover, and manure input mixing. The average manure temperatures were higher than air temperatures in the summer and fall. This indicated that using air temperature would result in an underestimation of methane emissions when applying the 2019 IPCC Refinement methodology. The revised model estimated manure temperatures for spring, summer, fall, and winter as 4.8, 16.1, 7.8, and 2.6 °C at the VA tank and 11.6, 17.1, 9.5, and 3.6 °C at the OR tank. The root mean square errors between daily simulated and observed temperatures in the summer decreased in both tanks due to incorporating shadow effect into the revised model. Fall estimates did not improve, possibly because of uncertainties from slurry removal and higher precipitation inputs. Sensitivity analysis indicated that solar radiative heat input was reduced with higher tank walls and smaller tank diameters when applying the revised model. Wall shading may influence manure temperatures in tanks with small diameters at high-latitude locations. This study offers insights into understanding the relationship between manure temperatures and its thermal balance influenced by latitude, storage design, snow cover and mixing, and its implications for accurately estimating methane emissions. [Display omitted] • Including shading, snow cover and mixing effects improved manure temperature predictions. • Solar radiative input is susceptible to manure storage wall height and diameter. • The revised model improved the accuracy of summer manure temperature estimates. • Fall manure temperature estimates remain unreliable due to uncertainty in slurry volume. [ABSTRACT FROM AUTHOR]

We present chironomid-based reconstructions of mean July air temperature changes over the last 2000 years from Lake Latoriței (1530 m a.s.l.) in the Southern Carpathians. A multi-proxy analysis was performed along a 58 cm long sediment core and two training sets were used for quantitative July air temperature reconstructions: the Eastern-European (EE, 212 lakes) and the Finnish-Polish-Carpathian (FPC, 273 lakes). The transfer functions had a coefficient of determination (r 2) 0.88 and 0.91 with a root mean squared error of prediction (RMSEP) 0.88°C and 1.02°C. Despite possible biases resulting from methodological problems and the ecological complexity of the chironomid response to both climatic and environmental changes, the agreement of the temperature reconstruction of Lake Latoriței with other alpine records suggests that the transfer function successfully reconstructed past summer temperatures between 750 and 1830 CE. Biases in the temperature reconstruction in the period before 750 and after 1830 CE were likely caused by increased abundance of rheophilic and semi-terrestrial chironomid species related to increased inflow activity before 750 CE and local land use changes after 1830 CE, which was also indicated by increasing deforestation and increasing lake productivity in the pollen and diatom records. Our results suggest that the region experienced a warm period between 750 and 1360 CE, and a cold period between 1360 and 1600 CE followed by fluctuating summer temperatures until 1830 CE. These events were associated with the so-called 'Mediaeval Warm Period' (MWP) and the 'Little Ice Age' (LIA), respectively. The inference models reconstructed a decrease in July air temperatures by 0.7°C–1.1°C during the LIA relative to the warmer MWP. We also demonstrated that the FPC training set gives better results, supporting that local/continental training sets are efficient to detect weak amplitude summer temperature changes in the Late-Holocene. [ABSTRACT FROM AUTHOR]

The thermal conditions of south-western Greenland in the second half of the 18th century were estimated using two unique series of meteorological observations. The first series (Neu-Herrnhut, 1 September 1767 to 22 July 1768, hereinafter 1767–1768) is the oldest long-term series of instrumental measurements of air temperature available for Greenland. The second (Godthaab, September 1784 to June 1792) contains the most significant and reliable data for Greenland for the study period. The quality-controlled and corrected data were used to calculate daily, monthly, seasonal and yearly means. The daily means were further used to calculate day-to-day temperature variability (DDTV), thermal seasons, growing degree days (GDDs), the air thawing index (ATI), positive degree days (PDDs) and air freezing index (AFI) degree days. Air temperature in Godthaab (now Nuuk) was, on average, warmer than the present day (1991–2020) in 1767–1768 and colder in 1784–1792. Compared to the early 20th-century Arctic warming (ETCAW) period, the data for the two sub-periods show that the late 18th century was as warm or even warmer. Except winter 1767/1768, winters and springs in the study period were longer, while summers and autumns were shorter than at present. The analysed climate indices usually did not exceed the maximum and minimum values from 1991–2020. Mean daily air temperature in the studied historical periods rarely exceed ±2 SD (standard deviation) of the long-term mean calculated for the contemporary period. Air temperature distribution was usually close to normal, in both historical and contemporary periods. [ABSTRACT FROM AUTHOR]

A precise forecast of atmospheric temperatures is essential for various applications such as agriculture, energy, public health, and transportation. Modern advancements in technology have led to the development of sensors and other tools to collect high-frequency air temperature data. However, accurate forecasts are challenging due to their specific features including high dimensionality, non-linearity, seasonal dependency, etc. To address these forecasting challenges, this study proposes a functional modeling framework based on the components estimation technique by partitioning the air temperature time series into deterministic and stochastic components. The deterministic component that comprises daily and yearly seasonalities is modeled and forecasted using generalized additive modeling techniques. Similarly, the stochastic component that accounts for the short-term dynamics of the process is modeled and forecasted by a functional autoregressive model, autoregressive integrated moving average, and vector autoregressive models. To evaluate the performance of models, hourly air temperature data are collected from Islamabad, Pakistan, and one-day-ahead out-of-sample forecasts are obtained for a complete year. The forecasting results from all models are compared using the root mean squared error, mean absolute error, and mean absolute percentage error. The results suggest that the proposed FAR model performs relatively well compared to ARIMA and VAR models, resulting in lower out-of-sample forecasting errors. The findings of this research can facilitate informed decision-making across sectors, optimize resource allocation, enhance public safety, and promote socio-economic resilience. [ABSTRACT FROM AUTHOR]

In this study, a novel method for retrieving atmospheric temperature profiles with tree-structured Parzen estimator (TPE) and multilayer perceptron (MLP) algorithms was proposed, using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) and ERA5 data. Firstly, by adding solar altitude angle, satellite zenith angle, 2m temperature, and surface temperature to the input layer of MLP, there is an improvement in retrieval accuracy. Secondly, TPE is effective in optimizing the hyper-parameters of MLP, and a set of optimized hyper-parameters is obtained through iterative optimization. Thirdly, comparing the retrieved temperature profiles with ERA5 data, we found that retrieval accuracy is influenced by detector, signal-to-noise ratio, terrain, solar altitude angle, satellite zenith angle, and the horizontal temperature gradient. The mean biases of the two adjacent detectors show significant differences, and the retrieval accuracy of the center detectors is greater than that of the north and south sides. The retrieval accuracy is relatively poor in areas with high terrain and large satellite zenith angle. There is a monthly variation in the retrieval accuracy due to the horizontal temperature gradient and signal-to-noise ratio and a significant diurnal variation due to solar altitude angle and signal-to-noise ratio. Compared to in situ sounding data, the mean biases vary from −0.56 K to 0.60 K, and the standard deviations vary from 1.26 K to 2.17 K. The analysis of factors influencing retrieval accuracy provides important insights into improving the ability to retrieve atmospheric temperatures from geostationary hyperspectral IR sounder observations for near real-time (NRT) applications. [ABSTRACT FROM AUTHOR]

In this study, we develop an Atmospheric Motion Vector (AMV)-based method for retrieving wind vectors using 183.31 GHz water-vapor absorption channels. The method involves tracking water-vapor features from image triplets and subsequently deriving wind fields from motion vectors. The height of the derived wind for each channel is determined by calculating the weighing function peak using monthly averaged ERA5 reanalysis data. By utilizing Microwave Humidity Sounder-II (MWHS-II) brightness temperatures from the five channels centered around 183.31 GHz, wind vectors are retrieved within the Arctic region for the entire year of 2022. The retrieval quality is evaluated through comparative analysis with ERA5 reanalysis data and the Visible Infrared Imaging Radiometer Suite (VIIRS) wind product. The resultant vector root mean square errors (RMSEs) are approximately 4.5 m/s for the three lower-height channels and 5.5 m/s for the two upper-height channels. These findings demonstrate a wind retrieval performance comparable to the existing methods, highlighting its potential for augmenting wind availability at lower height levels. [ABSTRACT FROM AUTHOR]

Modern challenges in climate prediction necessitate the adoption of advanced deep learning architectures for enhanced precision in temperature forecasting. This study undertakes a comparative evaluation of various neural network designs, particularly focusing on Deep Recurrent Neural Networks (DRNN) and their extension with Gated Recurrent Units (DRNN-GRU), chosen for their proven efficacy in sequential data analysis and long-term dependency capture. Leveraging a comprehensive meteorological dataset, collected from 1961 to 2023, which includes atmospheric temperature, pressure, and precipitation levels, the research unfolds a nuanced understanding of the climate variability. The evaluation framework rigorously applies Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) metrics to quantify model performance. The DRNN and DRNN-GRU architectures are distinguished for their superior predictive accuracy, suggesting their high potential for real-world forecasting applications. These findings are not merely academic; they imply substantial practical implications, particularly for geographic information systems where they can enhance climate monitoring and resource management. The paper culminates with recommendations for dataset expansion and diversified analytical techniques, which are critical for refining the predictive prowess of these models. This research thereby sets a benchmark for future explorations in the field and directs towards innovative avenues to augment the scientific understanding of climate dynamics. [ABSTRACT FROM AUTHOR]

Scientific methods must be used to forecast the greenhouse microclimate, which is influenced by crop management practices and the surrounding macroclimate, in order to produce a marketable yield. Using input parameters like indoor air temperature, relative humidity, solar radiation, indoor roof temperature, and indoor relative humidity, the MATLAB tool NARX was utilized in this study to predict the strawberry yield, indoor air temperature, relative humidity, and vapor pressure deficit. To increase the model's accuracy, the data were normalized. The Levenberg-Marquardt backpropagation algorithm was used to develop the model. A number of evaluation metrics, including the coefficient of determination, mean square error, root mean square error, mean absolute deviation, and Nash-Sutcliffe efficiency coefficient, were used to assess the models' accuracy. The outcomes demonstrated a high degree of accuracy of the models, with no discernible variation between the experimental and predicted values. It was discovered that the vapor pressure deficit model was the most significant since it affects crop metabolic activities and its precision can be utilized as a parameter for indoor climate control. [ABSTRACT FROM AUTHOR]

• Filtering OH rotational temperature estimates perturbations having periods of 0.7–8.2 h. • Statistical approaches allow subtracting uncorrelated random noise. • Seasonal and interannual variations of residuals reflect climatology of correlated wave processes. Ground-based observations of the natural hydroxyl (OH) nightglow at altitudes of 85–90 km are used for deriving the rotational temperature of excited OH, which is close to the neutral atmospheric temperature. For filtering of mesoscale perturbations, we use differences between pairs of measured values of OH rotational temperature separated with fixed time intervals in the range of 0.5–2 h. The filtering is applied for studying mesoscale variations of temperature near the mesopause according to the data of spectral OH nightglow measurements at observatories of Zvenigorod (56°N, 37°E.) in the years 2004–2016, Tory (52°N, 103°E) in 2012–2017 and Maymaga (63°N, 130°E) in 2000–2015. Monthly-mean values and variances of temperature disturbances with periods 0.7–8.2 h are determined. Semiempirical and statistical approaches are used to estimate and subtract variances of the instrumental dark current noise and uncorrelated in time fluctuations. Seasonal and interannual variations in standard deviations of correlated in time mesoscale perturbations of the OH rotational temperature at the considered observational sites are studied. They can give information about multiyear changes in the activity of atmospheric acoustic-gravity waves propagating through the OH layer near the mesopause. [ABSTRACT FROM AUTHOR]

Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions. Plain Language Summary: Accurate data on the volume and temperature of river water are essential for understanding how changing river conditions affect water quality and freshwater ecosystems. However, direct measurements of river parameters are often lacking, leading researchers to rely on models for estimation. In this study, we utilized advanced models and techniques to compute daily water volume and temperature in 138 basins across Alaska, including the entirety of the Yukon River basin, spanning from 1990 to 2021. Our findings indicated that rivers in northern Alaska exhibited higher maximum summer water temperatures and more significant temperature fluctuations compared to those in the central and southern regions. Our analysis highlighted that adjusting air temperature and the model's internal variables were crucial in minimizing errors in river temperature prediction. We improved the accuracy of the river temperature model by applying a technique to refine the model output based on the limited available river measurements. Comparing our enhanced model to a simpler statistical approach, we observed superior performance once the necessary adjustments were implemented. Key Points: Our model system produced an accurate, high‐resolution, daily hindcast of Alaskan river temperature and discharge from 1990 to 2021We increased model performance by employing a new optimization of the River Basin Model and forcing it with a climate‐land surface modelA sensitivity analysis highlights important drivers of river temperatures in each region and the need for optimization [ABSTRACT FROM AUTHOR]

Most pest phenology models are temperature dependent. Generally, the air temperature at reference height is used to predict pest development, but the air temperature varies between inside and outside the crop canopy, where pests reside. Here, we sampled 3 rice planthopper species—Nilaparvata lugens (Stål), Sogatella furcifera (Horváth), and Laodelphax striatellus (Fallén)—and micrometeorological observations in paddy fields to analyze how thermal environments inside the canopy affect pest development. Seasonal variations in the population density of these species were surveyed in 3 experimental fields with 2 water temperature plots (normal and low-water temperature plots).The development periods of the 3 species were predicted individually based on pest phenology models using temperatures recorded at 6 heights (0.0–2.0 m). We calculated the root mean square error (RMSE) values from the predicted and observed development periods for each rice planthopper. The development prediction using the temperature inside the canopy was more accurate than that utilizing the temperature at the reference height (2.0 m). In the low-water temperature plot, the RMSE value for N. lugens, S. furcifera, and L. striatellus was 6.4, 5.6, and 4.1 when using the temperature at the reference height (2.0 m), respectively, and 2.8, 3.8, and 2.9 when employing the temperature inside the canopy at 0.25 m, respectively. The development prediction utilizing the air temperature at the bottom (0.25 m) of canopy, where N. lugens resides, was most effective for N. lugens among the 3 species. These findings suggest the importance of utilizing microhabitat-based temperatures to predict pest development. [ABSTRACT FROM AUTHOR]

Local weather forecasts in the Arctic outside of settlements are challenging due to the dearth of ground-level observation stations and high computational costs. During winter, these forecasts are critical to help prepare for potentially hazardous weather conditions, while in spring, these forecasts may be used to determine flood risk during annual snow melt. To this end, a hybrid VMD-WT-InceptionTime model is proposed for multi-horizon multivariate forecasting of remote-region temperatures in Alaska over short-term horizons (the next seven days). First, the Spearman correlation coefficient is employed to analyze the relationship between each input variable and the forecast target temperature. The most output-correlated input sequences are decomposed using variational mode decomposition (VMD) and, ultimately, wavelet transform (WT) to extract time-frequency patterns intrinsic in the raw inputs. The resulting sequences are fed into a deep InceptionTime model for short-term forecasting. This hybrid technique has been developed and evaluated using 35+ years of data from three locations in Alaska. Different experiments and performance benchmarks are conducted using deep learning models (e.g., Time Series Transformers, LSTM, MiniRocket), and statistical and conventional machine learning baselines (e.g., GBDT, SVR, ARIMA). All forecasting performances are assessed using four metrics: the root mean squared error, the mean absolute percentage error, the coefficient of determination, and the mean directional accuracy. Superior forecasting performance is achieved consistently using the proposed hybrid technique. [ABSTRACT FROM AUTHOR]

The seasonal temperature forecasting models in greenhouse based on multiple regression (MR), BP artificial neural networks (BPANN), random forest (RF) and support vector machine (SVM) were constructed using meteorological observations inside and outside the Venlo-type glass greenhouses from 27th February 2021 to 4th March 2023 at Nanjing University of Information Science & Technology, and then to validate all the models. The results showed that the fitting accuracy of the seasonal forecasting models for daily average and minimum air temperatures in the greenhouse was significantly higher than that of the seasonal forecasting models for daily maximum air temperature; the fitting accuracy of each model for air temperature inside the greenhouse in spring, summer and autumn was higher than that for winter. For the seasonal forecasting models of daily average air temperature and minimum air temperature, the fitting accuracy of the spring, summer and autumn forecasting models constructed by the four algorithms was higher, but the simulation effect of the RF model was more stable, and the coefficient of determination (R²) of the simulated values and the actual observed values were above 0.94, and the root mean square error (RMSE) and absolute error (MAE) were within 1.5 °C. For the seasonal forecast models of daily maximum temperature, the fitting accuracy of RF model for spring, summer and autumn was higher than other models in general, and its R² value was above 0.75. The MR model had higher accuracy for winter indoor air temperature and was more suitable for predicting winter greenhouse air temperature. The study concluded that it is feasible to choose the RF model to forecast the air temperature inside the glass greenhouse in spring, summer and autumn and the MR model to forecast the air temperature inside the glass greenhouse in winter, and this study can provide an important reference for the seasonal forecast of air temperature in Venlo-type glass greenhouse. [ABSTRACT FROM AUTHOR]

Climate data matching the scales at which organisms experience climatic conditions are often missing. Yet, such data on microclimatic conditions are required to better understand climate change impacts on biodiversity and ecosystem functioning. Here we combine a network of microclimate temperature measurements across different habitats and vertical heights with a novel radiative transfer model to map daily temperatures during the vegetation period at 10 m spatial resolution across Switzerland. Our results reveal strong horizontal and vertical variability in microclimate temperature, particularly for maximum temperatures at 5 cm above the ground and within the topsoil. Compared to macroclimate conditions as measured by weather stations outside forests, diurnal air and topsoil temperature ranges inside forests were reduced by up to 3.0 and 7.8 ∘ C, respectively, while below trees outside forests, e.g. in hedges and below solitary trees, this buffering effect was 1.8 and 7.2 ∘ C, respectively. We also found that, in open grasslands, maximum temperatures at 5 cm above ground are, on average, 3.4 ∘ C warmer than those of the macroclimate, suggesting that, in such habitats, heat exposure close to the ground is often underestimated when using macroclimatic data. Spatial interpolation was achieved by using a hybrid approach based on linear mixed-effect models with input from detailed radiation estimates from radiative transfer models that account for topographic and vegetation shading, as well as other predictor variables related to the macroclimate, topography, and vegetation height. After accounting for macroclimate effects, microclimate patterns were primarily driven by radiation, with particularly strong effects on maximum temperatures. Results from spatial block cross-validation revealed predictive accuracies as measured by root mean squared errors ranging from 1.18 to 3.43 ∘ C, with minimum temperatures being predicted more accurately overall than maximum temperatures. The microclimate-mapping methodology presented here enables a biologically relevant perspective when analysing climate–species interactions, which is expected to lead to a better understanding of biotic and ecosystem responses to climate and land use change. [ABSTRACT FROM AUTHOR]

In this paper, desorption isotherms of two jerky products were studied (whole-muscle jerky -- sample 1 and minced jerky -- sample 2). The work focused on the comparison of the Dynamic Dewpoint Isotherm (DDI) method and the Saturated Salt Slurry (SSS) method and testing the newly applied model for modelling desorption isotherms for dried meat. Data were statistically processed using 8 models [Guggenheim-Anderson-de Boer (GAB), Double Log Polynomial (DLP), Henderson, Chin, Smith, Oswin, Hasley, and newly applied model] and statistically evaluated using coefficient of determination (R2), root mean squared error (RMSE) and mean relative percentage deviation (P-value). The DLP model (25 °C) reached R² ≥ 0.999, P-value ≤ 1.84 for DDI, and R2 ≥ 0.998, P-value ≤ 4.37 for SSS method. The GAB model reached R² ≥ 0.997, P-value ≤ 2.58 for DDI, and for SSS method the GAB model reached R² ≥ 0.998, P-value ≤ 5.47. The new model reached P-value ≤ 5.73 for DDI and P-value ≤ 3.48 for SSS method. All models reached the P-value < 10% except for Smith and Chin models. The DDI method and newly applied model prove to be a suitable and precise approach to the evaluation of isotherms of dried meat products. [ABSTRACT FROM AUTHOR]

This research addresses the growing complexity and urgency of climate change's impact on water resources in arid regions. It combines advanced climate modelling, machine learning, and hydrological modelling to gain profound insights into temperature variations and precipitation patterns and their impacts on the runoff. Notably, it predicts a continuous rise in both maximum and minimum air temperatures until 2050, with minimum temperatures increasing more rapidly. It highlights a concerning trend of decreasing basin precipitation. Sophisticated hydrological models factor in land use, vegetation, and groundwater, offering nuanced insights into water availability, which signifies a detailed and comprehensive understanding of factors impacting water availability. This includes considerations of spatial variability, temporal dynamics, land use effects, vegetation dynamics, groundwater interactions, and the influence of climate change. The research integrates data from advanced climate models, machine learning, and real-time observations, and refers to continuously updated data from various sources, including weather stations, satellites, ground-based sensors, climate monitoring networks, and stream gauges, for accurate basin discharge simulations (Nash-Sutcliffe efficiency - NSERCP2.6 = 0.99, root mean square error - RMSERCP2.6 = 1.1, and coefficient of determination RRCP2:6² = 0.95 of representative concentration pathways 2.6 (RCP)). By uniting these approaches, the study offers valuable insights for policymakers, water resource managers, and local communities to adapt to and manage water resources in arid regions. [ABSTRACT FROM AUTHOR]

This article addresses the simulation of urban air temperatures with a focus on evaluating the Urban Weather Generator (UWG) model over Toulouse, France. As urban temperatures, influenced by factors like urbanization, anthropogenic heat release, and complex urban geometry, exhibit an urban heat island (UHI) effect, understanding and mitigating UHI become crucial. With increasing global warming and urban populations, aiding urban planners necessitates accurate simulations requiring data at the canyon level. The paper evaluates UWG's performance in simulating air temperatures under realistic conditions, emphasizing an operational context and a non-specialist user's perspective. The evaluation includes selecting the most suitable meteorological station, assessing the impact of the rural station choice, and conducting a sensitivity analysis of input parameters. The validation demonstrates good agreement, with a mean bias error (MBE) of 0.02 °C and a root mean square error (RMSE) of 1.73 °C. However, we highlight the fact that UWG performs better in a densely urbanized area, and exhibits limitations in sensitivity to urban surface parameter variations, particularly in less urbanized areas. [ABSTRACT FROM AUTHOR]

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths. [ABSTRACT FROM AUTHOR]

The crop water stress index is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN) are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, and canopy temperature). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. For the lower CWSI baseline of rice, a linear relationship between air and canopy temperature difference (Tc-Ta) and, air vapour pressure deficit (AVPD) was developed, whereas air temperature plus 3°C was taken for the upper CWSI baselines. The performance of SOM and FF-BP-ANN were compared by computing Nash–Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R2). It is found that FF-BP-ANN (R2 = 0.97, NSE = 0.92, d = 0.95, RMSE = 0.006) performs better than SOM (R2 = 0.88, NSE = 0.87, d = 0.9, RMSE = 0.075). [ABSTRACT FROM AUTHOR]

This paper selects the case of the Atlantic hurricane "Michael" in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecasting (WRF) model, the assimilation of GOES-16 atmospheric temperature-profile products was achieved by using three-dimensional variational (3DVar) and the ensemble transform Kalman filter/three-dimensional variational (ETKF/3DVAR) hybrid system (Hybrid) systems. And the impact of geostationary satellite GOES-16 atmospheric temperature-profile data assimilation on a hurricane forecast is evaluated. The results show that, during the hurricane, the root mean square errors of the GOES-16 atmospheric temperature profile are all within 2 k at the height of 200–1000 hPa, and the quality of the data is generally good. Assimilating the GOES-16 atmospheric temperature-profile data can indeed effectively improve the analysis increment and improve the prediction results. The assimilation increment obtained by the hybrid system has obvious "flow-dependent" characteristics, which can reasonably improve the initial field of the model. Its temperature increment has an obvious spiral structure, which is in line with the characteristics of the hurricane, and the adjustment of the wind field and geopotential height field is also more beneficial to the development of the hurricane. It has a positive impact on the forecast of track, intensity, and precipitation, and the hybrid system is improved more obviously. In addition, from the RMSE of the analysis field and the forecast field relative to the observation data of different elements, the hybrid system is superior to the 3DVar system. [ABSTRACT FROM AUTHOR]

The present study aims to explore the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting meteorological time series data exhibiting seasonal patterns. We compared the performance of SARIMA models with different configurations and evaluate their forecasting accuracy using real-world meteorological datasets for three different agroclimatic zones of Punjab (sub mountainous region, central region and south west region) was analyzed to forecast mean monthly maximum air temperature, minimum air temperature and rainfall. The weather data was used from 1984-2022 for sub-mountainous zone (Ballowal Saunkhri), 1970-2022 for Central zone (Ludhiana) and 1977-2022 for south west zone (Bathinda). The results provide insights into the suitability and limitations of SARIMA models for meteorological forecasting and offer practical recommendations for practitioners and researchers in the field. The goodness of fit was tested against residuals using Ljung-Box test. The accuracy of the model was tested using Mean Absolute Error (MAE) and root square mean error (RMSE). The model achieved Mean Absolute Errors (MAE) ranging from 0.61 to 0.78 for maximum temperature, 0.74 to 0.49 for minimum temperature, and 32.12 to 45.44 for rainfall, with lower MAE values indicating higher predictive accuracy. The fitted model was able to capture dynamics of the temperature time series and produce a sensible forecast. However, the model was unable to forecast rainfall series efficiently. [ABSTRACT FROM AUTHOR]

The inversion data of the microwave radiometer atmospheric temperature profile were systematically analyzed with the temperature observation data of the microwave radiometer in coastal areas of Fujian. By comparing the microwave radiometer temperature inversion data with sounding and ERA5 temperatures, the ability of microwave radiometer to detect atmospheric temperature and the quality of the inversion data were analyzed, and the microwave radiometer inversion data were further applied to analyze the daily variation of atmospheric temperature in the boundary layer. The results show that the microwave radiometer temperature is in good agreement with the sounding temperature, with a correlation coefficient of 0.971, a mean deviation of 2.716 °C, and a root mean square error of 5.131 °C; the greater the intensity of rainfall, the more significant the effect of rainfall on the accuracy of the microwave radiometer in detecting atmospheric temperature. The correlation coefficients, mean deviations and root mean square errors of ERA5 and microwave radiometer are consistent from month to month; the temperature deviations between ERA5 and microwave radiometer in the middle atmosphere are smaller than those in the near-surface and upper atmosphere in each month; above 2 km in height, the temperature profiles of ERA5 and microwave radiometer are consistent. Above 2 km in altitude, the trend of ERA5 and microwave radiometer temperature profiles are basically the same. Below 3 km altitude, the daily variation of summer temperature is smaller than that of winter temperature at the same altitude. With the increase of altitude, the variation of atmospheric temperature in summer is large and that in winter is small [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products. [ABSTRACT FROM AUTHOR]

In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)'s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD's SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD's SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region. [ABSTRACT FROM AUTHOR]

Varied origins have the ability to construct the continental red beds, such as paleoclimate, provenance, drainage status, etc. Reconstructing paleoclimate is the key to investigate the origin. Thus, this paper outlines the normal distribution constrained method (NDCM) to reconstruct paleoclimate quantitatively and accurately during the lower fourth sub-member of Shahejie (Es4x) in the Bonan Sag, which is in low requirement of data and environmental conditions. Based on the NDCM, the paleoclimate is still in a long-term arid background as the potential evapotranspiration rates (PERs) are larger than 6 in the transgressive system tract (TST) and regressive system tract (RST) during the period of Es4x. The decreasing PERs that range from 6.28 to 6.04, decreasing atmospheric temperature range from 14.37 to 13.95 °C and increasing mean precipitation of the wettest month and mean precipitation of the driest month indicate the paleoclimate is breaking away from the hot and arid background from TST to RST. Meanwhile, the paleoclimate fluctuation develops decreasing frequency and increasing amplitude which can be inferred from the increasing standard deviation (std), Th/K and Th/U curves. The hot and arid background is still the main origin of red color during the Es4x. Moreover, the other origin may also develop since there is no clear law between red coloration and the developing location of sediments. [ABSTRACT FROM AUTHOR]

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Vapor pressure deficit (VPD) is the difference between saturation and actual water vapor pressures and is driven by interactions between air temperature and humidity. Globally, VPD observations indicate drying of the atmosphere, while air temperature (T) and relative humidity (RH) have changed asymmetrically during daytime versus nighttime. However, whether daytime or nighttime T (Tday and Tnight, respectively) and RH (RHday and RHnight, respectively) have been more important to VPD change is unknown, and so is the seasonal variation of this importance. This research determines the relative contribution of Tday, Tnight, RHday, and RHnight to VPD change observed during 1981–2021 across the conterminous United States. Tday contributed 41% to driving a VPD increase of 2.1 standard deviations (SD), followed by contributions of 25%, 22%, and 12% from RHday, RHnight, and Tnight, respectively. Regions with significant VPD increase have seen increase in Tday (by 1 SD) and Tnight (by 0.9 SD) and a decrease in RHday (by 1 SD) and RHnight (by 3.4 SD). Diurnal asymmetry in warming and drying trends was true for most months, with greater rates of change during the nighttime than daytime. Tday was the dominant driver of the annual mean VPD rise for 70% of the counties, followed by RHday (18%), RHnight (11%), and Tnight (1%). We conclude that warming and drying during the days and nights together explain increased VPD across the US croplands and that climate stressors during days and nights should be viewed independently due to their crop‐specific impacts that vary spatially and seasonally. Core Ideas: Vapor pressure deficit (VPD) has increased by 2.1 standard deviations during 1981–2021 in conterminous United States.Daytime or daily maximum air temperature (Tday), daytime or daily minimum relative humidity (RHday), nighttime or daily maximum relative humidity (RHnight), and nighttime or daily minimum air temperature contributed 41%, 25%, 22%, and 12% to annual mean VPD trend.Strong asymmetric warming and drying, that is, greater trends during daytime than nighttime.Relative contribution of drivers toward VPD trends changed substantially during the year.VPD rise during July and August was attributed equally to Tday, RHday, and RHnight. [ABSTRACT FROM AUTHOR]

Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM2.5, SO2, NO2 and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) datasets with high spatial and temporal resolutions are needed in a wide range of applications, such as hydrology, ecology, agriculture, and climate change studies. Nonetheless, the density of weather station networks is insufficient, especially in sparsely populated regions, greatly limiting the accuracy of estimates of spatially distributed Ta. Due to their continuous spatial coverage, remotely sensed land surface temperature (LST) data provide the possibility of exploring spatial estimates of Ta. However, because of the complex interaction of land and climate, retrieval of Ta from the LST is still far from straightforward. The estimation accuracy varies greatly depending on the model, particularly for maximum Ta. This study estimated monthly average daily minimum temperature (Tmin), average daily maximum temperature (Tmax) and average daily mean temperature (Tmean) over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (MYD11A2) and some auxiliary data using an artificial neural network (ANN) model. The data from 2003 to 2010 were used to train the ANN models, while 2011 to 2012 weather station temperatures were used to test the trained model. The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean, with root mean square errors (RMSEs) of 1.04°C and 1.01°C, respectively. Moreover, the best RMSE of Tmax estimation was 1.27°C. Compared with the other two published Ta gridded datasets, the produced 1 km × 1 km dataset accurately captured both the temporal and spatial patterns of Ta. The RMSE of Tmin estimation was more sensitive to elevation, while that of Tmax was more sensitive to month. Except for land cover type as the input variable, which reduced the RMSE by approximately 0.01°C, the other vegetation-related variables did not improve the performance of the model. The results of this study indicated that ANN, a type of machine learning method, is effective for long-term and large-scale Ta estimation. [ABSTRACT FROM AUTHOR]

The Hartebeesthoek Radio Astronomy Observatory (HartRAO) of South Africa is developing a Lunar Laser Ranging (LLR) system to achieve sub-centimetre range precision to the Moon. Key to this high precision expectation, which includes improving the overall operational performance of its telescope, is the thermal analysis of the telescope structure. In this study, thermal sensors were mounted on the thermally-important areas of the tube structure to measure the tube displacements emanating from the varying ambient air temperatures. A laser distance-measurement system was used for this purpose. Results showed that while the optical tube undergoes structural changes with changes in temperature, the tube position closer to the place where the spider assembly is mounted is unevenly displaced in three directions. In particular, for the time period considered in this study, it was found that the relative displacements on average at prisms 1, 2 and 3 in the vertical direction were 2.5540 ± 0.0007 m, 1.3750 ± 0.0008 m and 1.9780 ± 0.0007 m, respectively. The corresponding standard deviation (SD) values of ±0.0007 m, ±0.0008 m and ±0.0007 m denotes the average deviations that occurred in the vertical direction at the centre of prisms 1, 2 and 3, respectively. The generally higher SD of relative displacements in the vertical direction rather than in the easting and northing directions, suggest that the tube experienced greater variations in the vertical direction. Furthermore, the lower arc of the tube front, was found to have increased variability, and therefore it was hypothesised to introduce more elevation pointing offsets than azimuth for the LLR. This information constitutes an important input for guiding the efforts to determine the extent of the correction needed to be fed into the LLR telescope pointing model to counteract expected thermally induced pointing offsets. [ABSTRACT FROM AUTHOR]

The low-cost and more efficient adsorbents of Azadirachta Indica (Neem) are charcoal, bark; leave and root were investigated for its optimistic de-colorization of wastewater containing a dye. The effect of various parameters such as contact time, pH, temperature and adsorbent dosage has been carried out in this study. Two isotherm models were developed for compliance with Langmuir and Freundlich isotherm. The results of the goodness fit test based on Root Mean Square Error (RMSE), R² for Langmuir and Freundlich models were given as, for pH (0.9745 - 0.99919), (0.4831 - 0.9999) for Adsorbent Dose is (0.9034 - 0.9221), (0.4689 - 0.9884) for contact time (0.9494 - 0.9513), (0.4677 - 0.9990) and temperature (0.9264 - 0.9349), (0.3755 - 0.5849) respectively. Both models were found to be best fit on decolorization of dye with contact time, pH, temperature and adsorbent dosage. pH 5 value indicated that adsorbent was effective in the removal of dye. [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

The Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) is a well-established reanalysis dataset and is widely used for driving global-scale hydrological models. However, owing to its relatively coarse spatial resolution (0.5°), the capability of MERRA-2 is repeatedly challenged in regional-scale studies, especially for smaller areas of interest. In addition, the availability of in situ observation data is a pressing issue for generating meteorological forcing. We developed a grid-based high spatial (0.125°) and temporal (hourly) resolution meteorological forcing dataset, which can evaluate hydrological processes in South Korea using state-of-the-art meteorological observations from 1980 to 2020. The forcing dataset was created by combining Automated Synoptic Observing System (ASOS) in situ measurement data from the Korean Meteorological Administration and MERRA-2 reanalysis datasets. Five meteorological variables were provided in the ASOS-MERRA2 (precipitation, air temperature, surface pressure, specific humidity, and wind speed). The study demonstrates that the region-based and high spatial resolution of ASOS-MERRA2 is superior to the existing MERRA-2 with improvements of all five weather variables, for example, from 5.6 to 2.8 mm root mean square error of precipitation. The ASOS-MERRA2 was more capable of reducing the biases and root mean squared error by improving the coefficient of determination compared with MERRA-2 for all five variables. The newly developed ASOS-MERRA2 provides an opportunity to drive land surface models to evaluate the hydroclimatic conditions in South Korea. [ABSTRACT FROM AUTHOR]

Copyright of Nihon Chikusan Gakkaiho is the property of Japanese Society of Animal Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The atmospheric temperature profiles (ATPs) retrieved through the geostationary Interferometric Infrared Sounder (GIIRS) onboard the FY-4A satellite (GIIRS/FY-4A) can effectively fill the gap of the scarce conventional sounding data in the Taklimakan Desert (TD), the second largest desert in the world, with an area of 330,000 square kilometers. In this study, we take the experimental radiosonde observations (RAOB) from one RAOB station in the hinterland of TD and seven conventional radiosondes in the oasis region around the desert as the true values and analyze the bias distribution characteristics of GIIRS/FY-4A ATPs with quality control (QC) flags 0 or 1 for this region. In addition, a bias comparison is made with GIIRS/FY-4A ATPs, and the fifth generation ECMWF atmospheric reanalysis of the global climate (ERA5) ATPs. The results show that (1) Missing measurements in GIIRS/FY-4A ATPs are the most frequent in the near-surface layer, accounting for more than 80% of all the retrieved grid points. The averaged total proportion of GIIRS/FY-4A ATPs with QC marks 0 or 1 is about 33.06%. (2) The root mean square error (RMSE) of GIIRS/FY-4A ATPs is less than 3 K, smaller than that of ERA5 ATPs. The RMSE of ERA5 ATPs can exceed 10 K in the desert hinterland. The absolute mean biases of GIIRS/FY-4A ATPs and ERA5 ATPs are, respectively, smaller than 3 K and 2 K, the former being slightly larger. The correlation coefficients of GIIRS/FY-4A ATPs with ERA5 ATPs and RAOB ATPs are higher than 0.98 and 0.99, respectively, and the correlation between GIIRS/FY-4A ATPs and RAOB ATPs is inferior to the latter. (3) The overall atmospheric temperature retrieved by GIIRS/FY-4A is 0.08 K higher than the temperature of RAOB, on average, while the overall temperature from ERA5 is 0.13 K lower than that of RAOB, indicating that the temperature profile obtained by integrating GIIRS/FY-4A ATPs and ERA5 ATPs may be much closer to RAOB ATPs. (4) The probability density of the GIIRS/FY-4A ATP biases in the TD region generally follows the Gaussian distribution so that it can be effectively assimilated in the 3-D variational assimilation modules. The probability density distribution characteristics of the GIIRS/FY-4A ATP biases in the desert hinterland and oasis are not much different. However, due to the fusion analysis of the relatively rich multi-source conventional observation data from the oasis stations, the probability density of ERA5 ATPs biases at the oasis stations is nearer to Gaussian distribution than that of the GIIRS/FY-4A ATPs. In the desert hinterland, where conventional observation is not enough, the probability density distributions of the ATPs biases from ERA5 and GIIRS/FY-4A are alike. Therefore, the GIIRS FY4A can contribute to a more accurate estimation of ERA5 ATPs in the TD region. [ABSTRACT FROM AUTHOR]

Air temperature ( T a ) is a common meteorological element involved in many fields, such as surface energy exchange and water circulation. Consequently, accurate T a estimation is essential for the establishment of hydrological, climate, and environmental models. Unlike most studies concerned with the estimation of daily T a from land surface temperature, this study focused on the estimation of instantaneous T a from Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profile products aboard the Terra and Aqua satellites. The applicability of various estimation methods was examined in two regions with different geomorphological and climate conditions, North and Southwest China. Specifically, the spatiotemporal trend of T a under clear sky conditions can be reflected by the atmospheric profile extrapolation and average methods. However, the accuracy of T a estimation was poor, with root mean square error (RMSE) ranging from 3.5 to 5.2 °C for North China and from 4.0 to 7.7 °C for Southwest China. The multiple linear regression model significantly improved the accuracy of T a estimation by introducing auxiliary data, resulting in RMSE of 1.6 and 1.5 °C in North China and RMSE of 2.2 and 2.3 °C in Southwest China for the Terra and Aqua datasets, respectively. Since atmospheric profile products only provide information under clear sky conditions, a new multiple linear regression model was established to estimate the instantaneous T a under cloudy sky conditions independently from atmospheric profile products, resulting in RMSE of 1.9 and 1.9 °C in North China and RMSE of 2.5 and 2.8 °C in Southwest China, for the Terra and Aqua datasets, respectively. Finally, instantaneous T a products with high accuracy were generated for all-weather conditions in the study regions to analyze their T a spatial patterns. The accuracy of T a estimation varies depending on MODIS datasets, regions, elevation, and land cover types. [ABSTRACT FROM AUTHOR]

Reliable disease management can guarantee healthy plant production and relies on the knowledge of pathogen prevalence. Modeling the dynamic changes in spore concentration is available for realizing this purpose. We present a novel model based on a time-series modeling machine learning method, i.e., a long short-term memory (LSTM) network, to analyze oomycete Plasmopara viticola sporangia concentration dynamics using data from a 4-year field experiment trial in North China. Principal component analysis (PCA)-based high-quality input screening and simulation result calibration were performed to ensure model performance, obtaining a high determination coefficient (0.99), a low root mean square error (0.87), and a low mean bias error (0.55), high sensitivity (91.5%), and high specificity (96.5%). The impact of the variability of relative factors on daily P. viticola sporangia concentrations was analyzed, confirming that a low daily mean air temperature restricts pathogen development even during a long period of high humidity in the field. [ABSTRACT FROM AUTHOR]

This study aims to assess the accuracy of the reference evapotranspiration (ET0) estimated by CLDAS, ERA5 reanalysis products, and the quality of reanalysis weather variables required to calculate PM-ET0. For this purpose, the applicability of surface meteorological elements from the ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and the second-generation China Meteorological Administration Land Data Assimilation System (CLDASV2.0) datasets are evaluated in China by comparison with local observations from 689 stations reported by the Chinese Meteorological Administration (CMA). Statistics including percent bias (PBias), coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) are used to check the accuracy. The results show the highest correlation between reanalysis temperature and observations, with a mean R2 of 0.96, 0.90 for the CLDAS maximum and minimum air temperatures, and 0.87, 0.84 for ERA5. For the reanalysis of solar radiation (Rs) and relative humidity (RH), an overestimation trend is shown for Rs and an underestimation trend is shown for RH. For reanalysis of wind speed, a relatively low accuracy is shown. The accuracy of ET0 estimated by the two reanalysis products is acceptable in China, but the spatial and temporal consistency between the CLDAS estimates and site observations is higher, with a mean RMSE R2 of 0.91, 0.82 for CLDAS and 1.42, 0.70 for ERA5, respectively. Moreover, CLDAS reanalysis products are more effective in describing the boundary details of the study area. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of 6 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating temperature, precipitation, wind speed, and relative humidity over 29 cities in Indonesia. Modern-Era Retrospective Analysis for Research Applications (MERRA-2) was considered as reference data to assess the city-wise performance of surface air temperature, precipitation, wind speed, and relative humidity simulated by the CMIP6 GCMs during 1980–2014. Six statistical measures were computed in this process (mean annual, seasonal amplitude, mean annual bias, root mean square error, correlation coefficient, and standard deviation). For 29 cities, the mean annual values of surface air temperature, precipitation, wind speed, and relative humidity obtained from the GCMs range between 290 to 302 K, 100 cm to 450 cm, 1 to 6 m/s, and 70 to 94%, respectively. The correlation coefficient between the GCMs and the surface air temperature (precipitation) reanalysis dataset ranges from 0.3 to 0.85 (−0.14 to 0.77). The correlation coefficient for wind speed (relative humidity) varies from 0.2 to 0.6 and is positive in some cases (0.2 to 0.8). Subsequently, the relative error that combines the statistical measurement results was calculated for each city and meteorological variable. Results show that for surface air temperature and precipitation, the performance of TaiESM was outstanding over the 10 or more cities. In contrast, for wind speed and relative humidity, NOR-MM and MPI-HR were the best over 7 and 19 cities, respectively. For all the meteorological variables, the performance of AWI was found to be worst over all the cities. The outcomes of this study are essential for climate-resilience planning and GCM selection while performing downscaling experiments. It will also be useful for producing updated national climate change projections for each city in Indonesia and providing new insights into the climate system. [ABSTRACT FROM AUTHOR]

Accurate simulations of snow emission in surface-sensitive microwave channels are needed to separate snow from atmospheric information essential for numerical weather prediction. Measurements from a field campaign in Trail Valley Creek, Inuvik, Canada during March 2018 were used to evaluate the Snow Microwave Radiative Transfer (SMRT) Model at 89 GHz and, for the first time, frequencies between 118 and 243 GHz. In situ data from 29 snow pits, including snow specific surface area, were used to calculate exponential correlation lengths to represent the snow microstructure and to initialize snowpacks for simulation with SMRT. Measured variability in snowpack properties was used to estimate uncertainty in the simulations. SMRT was coupled with the Atmospheric Radiative Transfer Simulator to account for the directionally-dependent emission and attenuation of radiation by the atmosphere. This is a major developmental step needed for top-of-atmosphere simulations of microwave brightness temperature at atmosphere-sensitive frequencies with SMRT. Nadir simulated brightness temperatures at 89, 118, 157, 183 and 243 GHz were compared with airborne measurements and with ground-based measurements at 89 GHz. Inclusion of an anisotropic atmosphere in SMRT had the greatest impact on brightness temperature simulations at 183 GHz and the least at 89 GHz. Simulations compared well with observations, with a root mean squared error of 14 K, although snowpit measurements did not capture the observed variability fully as simulations and airborne observations formed statistically different distributions. Topographical differences in simulated brightness temperature between sloped, valley and plateau areas diminished with increasing frequency as the penetration depth within the snow decreased and less emission from the underlying ground contributed to the airborne observations. Observed brightness temperature differences between flights were attributed to the deposition of a thin layer of very low density snow. This illustrates the need to account for both temporal and spatial variability in surface snow microstructure at these frequencies. Sensitivity to snow properties and the ability to reflect changes in observed brightness temperature across the frequency range for different landscapes, as demonstrated by SMRT, is a necessary condition for inclusion of atmospheric measurements at surface-sensitive frequencies in numerical weather prediction. [ABSTRACT FROM AUTHOR]

Accurate estimation of evaporation is of great significance for understanding regional drought, and managing and applying limited water resources in dryland. However, the application of the traditional estimation approaches is limited due to the lack of required meteorological parameters or experimental conditions. In this study, a novel hybrid model was proposed to estimate the monthly pan Ep in dryland by integrating long short-term memory (LSTM) with grey wolf optimizer (GWO) algorithm and Kendall-τ correlation coefficient, where the GWO algorithm was employed to find the optimal hyper-parameters of LSTM, and Kendall-τ correlation coefficient was used to determine the input combination of meteorological variables. The model performance was compared to the performance of other methods based on the evaluation metrics, including root mean squared error (RMSE), the normalized mean squared error (NMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and Nash–Sutcliffe coefficient of efficiency (NSCE). The results indicated that the optimal input meteorological parameters of the hybrid Kendall-τ-GWO-LSTM models are the monthly average temperature, the minimum air temperature, the maximum air temperature, the minimum values of RMSE, NMSE, MAE, and MAPE are 38.28, 0.20, 26.62, and 19.96%, and the maximum NSCE is 0.89, suggesting that the hybrid Kendall-τ-GWO-LSTM exhibit better model performance than the other hybrid models. Thus, the hybrid Kendall-τ-GWO-LSTM model was highly recommended for estimating pan Ep with limited meteorological information in dryland. The present investigation provides a novel method to estimate the monthly pan Ep with limited meteorological variables in dryland by coupling a deep learning model with meta-heuristic algorithms and the data preprocessing techniques. [ABSTRACT FROM AUTHOR]

The onset of periods with very high or low air temperatures has aroused general interest for a long time, which is understandable since it has many dangerous effects directly affecting humans. Such perilous climatic phenomena include exceptionally cold and exceptionally warm months, which this study investigates for all Europe and for its five physico‐geographic regions over the 190‐year period of 1831–2020. Therefore, the research in this paper includes two periods characteristic of the history of climate – the late Little Ice Age (LIA) and present‐day warming. The studies are based on average monthly air temperature values from 40 weather stations in Europe. In this paper, exceptionally cold months (ECMs) or exceptionally warm months (EWMs) are considered to have occurred when the average air temperature at a station differed from the respective long‐term average by at least two standard deviations. The highlights of the study include the identification of a drop in the number of ECMs by 20 over the entire 190‐year period, and a highly statistically significant increase in EWMs by 44 between 1980 and 2020. These changes proceeded with different intensities from one physico‐geographic region of Europe to another. [ABSTRACT FROM AUTHOR]

In mountainous regions, the scarcity of air temperature (Ta) measurements is a major limitation for hydrological and crop monitoring. An alternative to in situ measurements could be to downscale the reanalysis Ta data provided at high-temporal resolution. However, the relatively coarse spatial resolution of these products (i.e., 9 km for ERA5-Land) is unlikely to be directly representative of actual local Ta patterns. To address this issue, this study presents a new spatial downscaling strategy of hourly ERA5-Land Ta data with a three-step procedure. First, the 9 km resolution ERA5 Ta is corrected at its original resolution by using a reference Ta derived from the elevation of the 9 km resolution grid and an in situ estimate over the area of the hourly Environmental Lapse Rate (ELR). Such a correction of 9 km resolution ERA5 Ta is trained using several machine learning techniques, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Extreme Gradient Boosting (Xgboost), as well as ancillary ERA5 data (daily mean, standard deviation, hourly ELR, and grid elevation). Next, the trained correction algorithms are run to correct 9 km resolution ERA5 Ta, and the corrected ERA5 Ta data are used to derive an updated ELR over the area (without using in situ Ta measurements). Third, the updated hourly ELR is used to disaggregate 9 km resolution corrected ERA5 Ta data at the 30-meter resolution of SRTM's Digital Elevation Model (DEM). The effectiveness of this method is assessed across the northern part of the High Atlas Mountains in central Morocco through (1) k-fold cross-validation against five years (2016 to 2020) of in situ hourly temperature readings and (2) comparison with classical downscaling methods based on a constant ELR. Our results indicate a significant enhancement in the spatial distribution of hourly local Ta. By comparing our model, which included Xgboost, SVR, and MLR, with the constant ELR-based downscaling approach, we were able to decrease the regional root mean square error from approximately 3 ∘ C to 1.61 ∘ C, 1.75 ∘ C, and 1.8 ∘ C, reduce the mean bias error from −0.5 ∘ C to null, and increase the coefficient of determination from 0.88 to 0.97, 0.96, and 0.96 for Xgboost, SVR, and MLR, respectively. [ABSTRACT FROM AUTHOR]

Evaporation is one of the main essential components of the hydrologic cycle. The study of this parameter has significant consequences for knowing reservoir level forecasts and water resource management. This study aimed to test the three artificial neural networks (feed-forward, Elman and nonlinear autoregressive network with exogenous inputs (NARX) models) and multiple linear regression to predict the rate of evaporation in the Boudaroua reservoir using the calculated values obtained from the energy budget method. The various combinations of meteorological data, including solar radiation, air temperature, relative humidity, and wind speed, are used for the training and testing of the model's studies. The architecture that was finally chosen for three types of neural networks has the 4-10-1 structure, with contents of 4 neurons in the input layer, 10 neurons in the hidden layer and 1 neuron in the output layer. The calculated evaporation rate presents a typical annual cycle, with low values in winter and high values in summer. Moreover, air temperature and solar radiation were identified as meteorological variables that mostly influenced the rate of evaporation in this reservoir, with an annual average equal to 4.67 mm·d-1. The performance evaluation criteria, including the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE) approved that all the networks studied were valid for the simulation of evaporation rate and gave better results than the multiple linear regression (MLR) models in the study area. [ABSTRACT FROM AUTHOR]