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The study of atmospheric models and climate systems has been hampered by the restricted availability of wind profile measurements. By recording wind profiles in near real time and giving useful information for Numerical Weather Prediction (NWP) models, the Doppler wind lidar technology used in the European Space Agency’s Aeolus mission is expected to unravel this problem. By analyzing the precipitation from IANOS medicane, which occurred over the Thessaly plain in September 2020, a case study utilizing the COSMO NWP model illustrates the potential usefulness of Aeolus data in strengthening NWP models. Run in hindcast mode and forced by analyses with and without Aeolus, the model assimilated data that were produced at the European Centre for Medium-Range Forecasts (ECMWF). Evaluation against observations from Greek synoptic stations showed that the model precipitation using the Aeolus-assimilated data preponderated over the model results without their inclusion. This work aims to demonstrate the additional value of the Aeolus project towards the expansion of our knowledge of Earth’s atmosphere, particularly the improvement of our capacity to estimate severe weather events via the use of Aeolus with NWP models.

We introduce a computational framework to forecast cloud index (CI)fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs)derived from a mesoscale numerical weather prediction (NWP)model and sparse optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. We describe how the method operates on three representative case studies and present results from 39 cloudy days.

We introduce a computational framework to forecast cloud index (CI)fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs)derived from a mesoscale numerical weather prediction (NWP)model and sparse optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. We describe how the method operates on three representative case studies and present results from 39 cloudy days.

This research extends the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) by adding the capability to assimilate aerosol optical depth (AOD) retrievals from the Geostationary Operational Environmental Satellite Series‐R (GOES‐R) satellites. The WoFS is a rapidly cycling, ensemble‐based analysis and forecasting system designed to generate short term (0–3 hr) forecasts of high impact weather. WoFS‐Smoke provides short‐term forecasts of smoke aerosols injected into the atmosphere from ongoing wildfires. The vertically integrated concentration of smoke aerosols in the atmosphere can be estimated from satellite based AOD retrievals. To assimilate AOD into WoFS‐Smoke, a smoke control variable is added to the prognostic state that is updated during each assimilation cycle. Then, a forward operator is created to relate modeled smoke aerosols to AOD retrievals using a mathematical function developed for the smoke‐AOD conversion used by the High‐Resolution Rapid Refresh for Smoke. Finally, GOES‐R AOD retrievals are quality controlled and are assimilated at 15 min intervals. Comparing analyzed AOD with smoke and other atmospheric variables indicates that assimilating AOD not only directly impacts smoke aerosol concentration in the system, but also has indirect impacts on variables such as temperature, humidity, and wind. Results from two wildfire cases in Oklahoma and Arizona show that assimilating AOD substantially impacts the concentration and distribution of smoke aerosols in the system. Forecast verification against satellite and surface observations indicates the overall impact of assimilating AOD in WoFS‐Smoke can improve forecast skill of smoke and the surrounding environment. Plain Language Summary: Short‐term forecasts of smoke generated from the Warn‐on‐Forecast System for Smoke (WoFS‐Smoke) data assimilation and forecasting system are improved through the assimilation of aerosol characteristics derived from satellites. Satellite measurements of aerosols provide information on the amount and distribution of aerosols in the atmosphere. Assimilating these data into a system designed to track smoke aerosols produces a more accurate analysis of smoke concentration. This work uses aerosol observations from a geostationary orbiting satellite to take advantage of the high temporal frequency (<15 min) needed to continuously update smoke in WoFS‐Smoke. Testing of two wildfire events showed that assimilating aerosol characteristics improved both smoke forecasts and forecasts of the surrounding atmospheric conditions. Key Points: Ensemble data assimilation of Geostationary Operational Environmental Satellite Series‐R aerosol optical depth (AOD) retrievals for short‐term smoke forecastsAssimilation of AOD improves the forecast of smoke aerosols in two wildfire casesImproved smoke forecasts impact forecasts of the surrounding atmospheric conditions [ABSTRACT FROM AUTHOR]

GPS PWV (Precipitable Water Vapour) is estimated using three years ECMWF, JRA55, MERRA2, and NCEP datasets for two IGS stations HYDE and IISC in India to assess their accuracies over the Indian region. The NWP derived temperature, pressure, zenith hydrostatic delay (ZHD), zenith wet delay (ZWD), and PWV values are validated using AWS observations and derived products. The obtained PWV values are also compared with the total column water product from ECMWF. The results show absolute mean temperature differences of 1-2 °K and pressure differences of 1-4 hPa for HYDE and IISC. NWP models' pressure and temperature values strongly correlate with AWS values with R² close to 0.9 in most of the cases. The estimated zenith tropospheric delay (ZTD) shows an average difference between 4mm and 9mm with respect to IGS ZTD. The mean differences between NWP and reference PWV values are observed from 1mm to 3 mm. All the derived products obtained using NWP datasets are close to the reference AWS products. However, PWV derived using ECMWF, JRA55 and NCEP is very much close to AWS PWV and can be used for precise PWV computation in this region. [ABSTRACT FROM AUTHOR]

This is a comprehensive approach for the analysis of fog attributes with high-density intra-hourly data (Oct–Mar) for a period of 11 years from Jan 2009 to Dec 2019 in sub-urban Bengaluru. Also, the characteristics of nocturnal inversion parameters have been examined on the days with/without fog to explore any relationship between the two concurring meteorological phenomena. Lastly, ability of NWP model (NCMRWF, Unified Model, NCUM) was examined for predicting fog over Kempegowda International Airport, Bengaluru. Under the studied time frame, December reported 129 and January 126 fog events, while October, November, February, and March reported 33, 96, 44, 11 events, respectively. Similarly, December and January accounted for maximum fog hours. More than 80% of fog events were reported between 2200 and 0200 UTC with maximum (74) in the 0100–0129 UTC slot and dispersed between 0000 and 0400 UTC. Intensity-wise, shallow fog is most frequent (51%), while moderate, dense, and very-dense have a frequency of 29, 19.6 and 0.4%, respectively. As for atmospheric inversions in Bengaluru, elevated inversions are common occurrences throughout the year, while surface inversions are more frequent in the winter. In the study, it was found that 35% fog events coincided with two elevated inversions (EI) under the 700 hPa ceiling, whereas 25% occurred with a single EI. Surface inversions coinciding with fog events are relatively rare and mostly (71%) occur alongside EI. NWP model predicted reduced visibility during fog events with a hit rate of 42%, false alarm rate of 5% and bias value of 0.76. Model produced mixed results in forecasting of meteorological parameters and visibility. Wind speed and visibility were underestimated, whereas temperature and relative humidity show good agreement with the observed values. [ABSTRACT FROM AUTHOR]

The multi-model ensemble (MME) forecast for meteorological elements has been proved many times to be more skillful than the single model. It improves the forecast quality by integrating multiple sets of numerical forecast results with different spatial-temporal characteristics. Currently, the main numerical forecast results present a grid structure formed by longitude and latitude lines in space and a special two-dimensional time structure in time, namely the initial time and the lead time, compared with the traditional one-dimensional time. These characteristics mean that many MME methods have limitations in further improving forecast quality. Focusing on this problem, we propose a deep MME forecast method that suits the special structure. At spatial level, our model uses window self-attention and shifted window attention to aggregate information. At temporal level, we propose a recurrent like neural network with rolling structure (Roll-RLNN) which is more suitable for two-dimensional time structure that widely exists in the institutions of numerical weather prediction (NWP) with running service. In this paper, we test the MME forecast for sea level pressure as the forecast characteristics of the essential meteorological element vary clearly across institutions, and the results show that our model structure is effective and can make significant forecast improvements. [ABSTRACT FROM AUTHOR]

Two‐fluid modelling has recently emerged as a promising approach to representing cumulus convection in weather and climate models. This study applies the two‐fluid model described in Part II to a shallow cumulus convection case study over land (ARM). Large‐eddy simulation data are used to tune the majority of the closures that determine the properties of entrained and detrained air. The two‐fluid model is generally able to reproduce the profiles of the mean and turbulent quantities over all stages of the diurnal cycle. As such, the initiation of shallow convection and the evolution of the cloud layer are well captured. The robustness of the two‐fluid model is verified further using a steady‐state test case (BOMEX), in which the cloud properties are also well modelled. [ABSTRACT FROM AUTHOR]

Observation system experiments (OSEs) are conducted to assess the potential impacts of horizontal line‐of‐sight wind profile observations from the Aeolus satellite on tropical cyclone (TC) forecasting. The OSEs utilize the operational Hurricane Weather and Research Forecasting (HWRF) model. The OSEs include 226 forecasts from seven TC cases in the Atlantic and Eastern Pacific basins. Comparisons between Aeolus and model background winds show that winds from Aeolus are consistently stronger than those from HWRF. Data assimilation statistics also demonstrate that the greatest potential impacts from the assimilation of Aeolus observations are likely to occur in the upper troposphere and lower stratosphere and within approximately 500 km from the TC centre. For TC forecasting applications, the assimilation of Aeolus observations improves TC intensity and size forecasts in the Eastern Pacific basin, while the results for track forecasts and results from the Atlantic basin are mixed. However, in both basins, the largest and most statistically significant, positive impacts from the assimilation of Aeolus observations occur when reconnaissance flight data are unavailable and during the early stages of TC development. The traditionally used forecast assessments of TC intensity, track and size are rooted in surface‐based metrics, and an additional investigation above the surface demonstrated larger improvements from assimilating Aeolus observations on TC wind structure above 400 hPa as compared to the lower troposphere. Several, different assessments throughout this study demonstrate higher uncertainty and the need for special consideration associated with assimilation techniques within 500 km from the TC centre. [ABSTRACT FROM AUTHOR]

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

The European Space Agency’s AEOLUS mission provides vertical profiles of the horizontal line-of-sight (HLOS) wind component in the troposphere and lower stratosphere, as well as secondary products with retrievals of extinction and backscatter coefficients. Under the scope of the ESA L2A+ project, we present an assimilation system of both wind and aerosol information from AEOLUS in a regional numerical weather prediction model (WRF). This study aims to highlight the impact of such a dataset on desert dust transport through assimilation experiments over the broader North Atlantic Ocean region, which features high dust transport events through the Saharan Air Layer. The results will be validated through comparisons with observations from the ESA-ASKOS/JATAC experiments.

The limited availability of wind profile measurements has hindered atmospheric models and climate systems’ understanding. The European Space Agency’s Aeolus mission’s space-based Doppler wind lidar technology could solve this issue by measuring wind profiles in Near-Real-Time, providing valuable data for Numerical Weather Prediction (NWP) models. A case study using the COSMO NWP model demonstrates the potential of Aeolus data in improving NWP models by examining the impact of Medicane IANOS in September 2020 over the Thessaly plain. The study aims to improve our ability to predict severe weather events and advance our understanding of Earth’s atmosphere.

This study aims at assessing meteorological, hydrological, and hydraulic modeling to develop a flash flood forecasting tool. The flash flood that occurred in the Evrotas River Basin (ERB) on 26 January 2023 is used as a case study. Precipitation over 150 mm and water depths exceeding 2.5 m were recorded. The meteorological model initialized one day before flooding and simulated precipitation; the hydrological model, using meteorological input data, simulated discharge; and the hydraulic model, using discharge, estimated water depth at a bridge. The results indicate that the system can provide skillful and timely flash flood forecasts, thereby facilitating flood warnings.

This study investigates the impact of assimilating Aircraft Meteorological Data Relay (AMDAR) observations on the prediction of two typhoons, Nesat and Haitang (2017), using the Gridpoint Statistical Interpolation (GSI) assimilation system and the Weather Research and Forecasting (WRF) model. Two quality control (QC) methods, Minimum Covariance Determinant (MCD) and one-class Support Vector Machine (OCSVM), were employed to perform QC on the AMDAR observations before data assimilation. The QC results indicated that both methods significantly reduced kurtosis, skewness, and discrepancies between the AMDAR data and the reanalysis data. The data distribution after applying the MCD-QC method exhibited a closer resemblance to a Gaussian distribution. Four numerical experiments were conducted to assess the impact of different AMDAR data qualities on typhoon forecasting, including a control experiment without data assimilation (EXP-CNTL), assimilating all AMDAR observations (EXP-RAW), assimilating observations after applying MCD-QC (EXP-MCD), and assimilating observations after applying OCSVM-QC (EXP-SVM). The results demonstrated that using AMDAR data in assimilation improved the track and intensity prediction of the typhoons. Furthermore, utilizing QC before assimilation enhanced the performance of track forecasting prediction, with EXP-MCD showing the best performance. As for intensity prediction, the three assimilation experiments exhibited varying strengths and weaknesses at different times, with EXP-MCD showing smaller intensity forecast errors on average.

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.

Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long-term prediction tasks. To address this issue, a power prediction model based on a Gated Transformer is proposed in this paper. Firstly, it can extract features from different types of data sources, effectively capture their correlations, and achieve data fusion. Secondly, gating unit, dilated convolution unit, and multi-head attention mechanism are added to improve the Receptive field and generalization ability of the model. In addition, adding a decoder to guide data prediction further improves the accuracy of prediction. Finally, experiments are carried out with the data collected from typical wind farms. The results show that the proposed Gated Transformer achieves consistent state-of-the-art results in prediction tasks on different time scales.

A new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts information about which inputs are most important for the prediction. It is trained using time series input from NWP variables and crowd‐sourced 2 m air temperature observations from more than 1000 private weather stations (PWSs). The performance of the new post‐processing model is evaluated using both observational data from PWSs and completely independent observations from the Danish Meteorological Institute (DMI) network of surface synoptic observations (SYNOP) stations. The performance of the Transformer model is compared against the raw NWP forecast, as well as against two benchmark post‐processing models; a linear regression (LR) model and a neural network (NN). The results evaluated using PWS observations show an improvement in the 2 m temperature forecasts with respect to both bias and standard deviation (STD) for all three post‐processing models, with the Transformer model showing the largest improvement. The raw NWP forecast, LR, NN and Transformer model have a bias and STD of 0.34 and 1.96°C, 0.03 and 1.63°C, 0.10 and 1.53°C and 0.02 and 1.13°C, respectively. The corresponding results using DMI SYNOP stations also show improved forecasts, where the Transformer model performs better than both the raw NWP forecast and the two benchmark models. However, a dependence on distance to the coast and cold temperatures is observed. [ABSTRACT FROM AUTHOR]

The Met Office held a testbed over winter 2020/2021 where a new numerical weather prediction (NWP) sub‐km ensemble was set up on‐demand in response to interesting weather phenomena in the United Kingdom. The domain for the model was chosen in real time by a community of Met Office Research Scientists and Operational Meteorologists and over a 4‐month period the ensemble was triggered for nine events. The purpose of the testbed was to investigate whether a real‐time weather regime‐based enhancement in NWP capability was feasible, to understand what benefits a testbed environment might give, and to explore the practicalities of running such an event. Case studies from the testbed demonstrated that forecast ensembles at 2.2 km and 300 m grid spacing were able to capture observed winter weather, with greater spatial detail apparent, especially over complex orography, in the 300‐m model. Ensemble spread appeared less influenced by resolution, potentially due to the size of the domains tested or the weather regimes of the case studies. The testbed also showcased underutilized observations and additional radiosonde ascents were conducted. All the testbed meetings were conducted virtually due to COVID‐19 restrictions, and decisions were made about when to trigger the event using an online message board. The winter 2020/2021 testbed provides ideas for how on‐demand weather‐dependent testbeds might be conducted in the future. However, several recommendations are made that would enhance testbed benefits further, including more dedicated resource, stronger technology and data visualization and greater participation from both academia and weather information users. [ABSTRACT FROM AUTHOR]

Data impact experiments are conducted employing the Polar Weather Research and Forecasting (WRF) model during the YOPP‐SH summer special observing period (SOP) using the Antarctic Mesoscale Prediction System (AMPS) framework to determine the forecast impact of numerous additional radiosondes collected during the SOP. Hybrid variational‐ensemble three‐dimensional data assimilation is performed on model forecast domains over Antarctica and the Southern Ocean using all regular observations normally available (Experiment "NoSOP") and using the same set plus the extra soundings launched for the SOP (Experiment "SOP"). The SOP results show better near‐surface temperature and wind‐speed forecasts than the NoSOP results, primarily over West Antarctica. Radiosonde profiles confirm that temperature and wind‐speed forecasts are improved throughout the troposphere with the addition of the SOP radiosonde data, but the results for relative humidity are variable. Temperatures are improved at lower levels early in the forecasts, whereas wind speeds are better at higher levels later in the forecasts. An evaluation against the ERA5 global reanalysis that provides a much broader perspective reveals that the improved forecast skill for the SOP experiment persists up to 72 hours for temperature, wind speed, and relative humidity. The gains, however, are primarily confined to the Antarctic continent, consistent with the additional radiosonde spatial coverage being mainly poleward of 60°S. With extra radiosondes concentrated over the Antarctic Peninsula, SOP forecasts of the region downstream of the Peninsula were significantly improved compared to NoSOP forecasts. In addition, it is found that the assimilation of the additional radiosonde data can have a greater impact on the forecasts of strong cyclones, as shown for a major coastal cyclone affecting West Antarctica, with improvements in its magnitude and track. The results also suggest that increasing radiosonde launches at lower southern latitudes would improve forecasts over the Southern Ocean, especially during austral winter. [ABSTRACT FROM AUTHOR]

Summary: In this research, an implementation of artificial deep neural networks (ANN) over outputs of 24‐h multi‐domain high‐resolution nested real case Weather Research and Forecasting (WRF) model runs was carried out over two high‐resolution simulation domains, which are tested and compared for rainfall generation in order to assess the signal fading event observed on geostationary telecommunication spacecraft in orbit for a real multiscale storm case. Our methodology of ANN, which is driven by WRF model output parameters, focuses on prediction of the rain attenuation signal impairment which is observed on the communication satellite telemetry (TM) downlink signal levels under significant aerosol presence due to dust storm which occurred on 12 September 2020. This modelling approach is then compared to rain attenuation observed on TM signal and correlated with communication satellite ground station TM signal measurements. Preliminary results from conducted error analysis (RMSE) on multiple input single output feed‐forward neural network (MISO FFNN) prediction model outputs tested with several neural algorithms indicate good correlation with the TM downlink signal attenuation observations taken from the ground station TM baseband demodulator. [ABSTRACT FROM AUTHOR]

The wind field of a waterspout that occurred near the Hong Kong International Airport was documented using dual-Doppler analysis from a dense network of Doppler Light Detection and Ranging (LIDAR) systems operated at the airport, and nearby surface weather station observations. The dual-Doppler winds are used in tandem with surface observations to analyze the wind structure of the waterspout. The performance of a numerical weather prediction system simulating the windshift line and the associated vortices is compared to the observations. The waterspout and the other vortices formed along a windshift line (convergence between the arriving northeast monsoon and background westerly/sea breeze) and were rather weak, with the surface wind gusting to about 10 m/s. However, the dense network of meteorological sensors at the airport provided unprecedented surface sampling of these vortices. The numerical model captured the convergence line and the vortices reasonably well and may provide earlier alerting to the aviation weather forecasters about the possible occurrence of small waterspout-type vortices near the airport, which may have implications to aviation safety.

Abstract The Met Office held a testbed over winter 2020/2021 where a new numerical weather prediction (NWP) sub‐km ensemble was set up on‐demand in response to interesting weather phenomena in the United Kingdom. The domain for the model was chosen in real time by a community of Met Office Research Scientists and Operational Meteorologists and over a 4‐month period the ensemble was triggered for nine events. The purpose of the testbed was to investigate whether a real‐time weather regime‐based enhancement in NWP capability was feasible, to understand what benefits a testbed environment might give, and to explore the practicalities of running such an event. Case studies from the testbed demonstrated that forecast ensembles at 2.2 km and 300 m grid spacing were able to capture observed winter weather, with greater spatial detail apparent, especially over complex orography, in the 300‐m model. Ensemble spread appeared less influenced by resolution, potentially due to the size of the domains tested or the weather regimes of the case studies. The testbed also showcased underutilized observations and additional radiosonde ascents were conducted. All the testbed meetings were conducted virtually due to COVID‐19 restrictions, and decisions were made about when to trigger the event using an online message board. The winter 2020/2021 testbed provides ideas for how on‐demand weather‐dependent testbeds might be conducted in the future. However, several recommendations are made that would enhance testbed benefits further, including more dedicated resource, stronger technology and data visualization and greater participation from both academia and weather information users.

Abstract A new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts information about which inputs are most important for the prediction. It is trained using time series input from NWP variables and crowd‐sourced 2 m air temperature observations from more than 1000 private weather stations (PWSs). The performance of the new post‐processing model is evaluated using both observational data from PWSs and completely independent observations from the Danish Meteorological Institute (DMI) network of surface synoptic observations (SYNOP) stations. The performance of the Transformer model is compared against the raw NWP forecast, as well as against two benchmark post‐processing models; a linear regression (LR) model and a neural network (NN). The results evaluated using PWS observations show an improvement in the 2 m temperature forecasts with respect to both bias and standard deviation (STD) for all three post‐processing models, with the Transformer model showing the largest improvement. The raw NWP forecast, LR, NN and Transformer model have a bias and STD of 0.34 and 1.96°C, 0.03 and 1.63°C, 0.10 and 1.53°C and 0.02 and 1.13°C, respectively. The corresponding results using DMI SYNOP stations also show improved forecasts, where the Transformer model performs better than both the raw NWP forecast and the two benchmark models. However, a dependence on distance to the coast and cold temperatures is observed.

The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS.

In spite of WRF-Solar being a numerical weather prediction (NWP) model specifically tailored for solar energy applications, the model lacks a specific cloud initialization component to improve short-range predictions. To overcome this limitation we have combined the fundamental concepts of the Multi-sensor Advection Diffusion nowCast (MADCast) model of using satellite observations to detect the clouds and advect and diffuse the clouds within a NWP model, with the better cloud–aerosol–radiation physics of WRF-Solar, into a new model referred to as MAD-WRF. The MAD-WRF cloud initialization combines a cloud parameterization that infers the presence of clouds based on relative humidity with observations of the cloud mask and cloud top/base height to provide a three-dimensional cloud analysis (liquid, ice, and snow hydrometeor content). During the forecasts, the hydrometeors can be advected and diffused with no microphysics, in what we refer to as the MAD-WRF passive mode. Alternatively, these passive hydrometeors can be integrated into the explicitly resolved hydrometeors during a nudging phase, designated the MAD-WRF active mode. In the nudging phase, there is a relaxation of the resolved hydrometeors towards the passive hydrometeors. Both modes aim to avoid an abrupt dissipation of clouds if the atmospheric environment is not adequate to support them. Both MAD-WRF active and passive modes as well as WRF-Solar have been run in a demonstration of MAD-WRF over the contiguous U.S., and results from these short-range forecasts (0–6 h) are presented here to illustrate the added value of MAD-WRF for global horizontal irradiance predictions. These results clearly illustrate the added value that MAD-WRF brings for short term irradiance predictions with the WRF-Solar model. • We have created the MAD-WRF model to improve cloud initialization in WRF-Solar. • MAD-WRF improves WRF-Solar short range irradiance predictions. • Consistent added value with respect to climatology. [ABSTRACT FROM AUTHOR]

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With ambitious renewable energy capacity addition targets, there is an ongoing transformation in the Indian power system. This paper discusses the various applications of variable generation forecast, state-of-the-art solar PV generation forecasting methods, latest developments in generation forecasting regulations and infrastructure, and the new challenges introduced by VRE generation. Day-ahead NWP-based GHI forecasting are validated against ground measurements from single and multiple sites in India. Recommendations for improving overall the forecasting infrastructure in India are presented. [ABSTRACT FROM AUTHOR]

Granularity of the grid (both horizontally and vertically) is a key consideration when conducting localised Numerical Weather Prediction (NWP) modelling. Generally speaking, an NWP model with a finer grid can explicitly resolve more processes and require less parameterisation. However, a finer grid also requires more computation and it is not always clear that a finer grid will produce a more accurate forecast. In this study, we explore the sensitivity of rainfall prediction over Singapore to grid resolution. We use the Weather and Research Forecasting model (WRF) to forecast rainfall over Singapore and explore the performance of horizontal resolutions ranging from 1 km to 12 km. We test the performance on a set of dates from across the years 2020–2021 against both ground observations and radar-derived rain rates. When compared to ground observations, we show that, overall, the higher resolution produces the highest Critical Success Index (CSI) for rain rates in excess of 0.5 mm/h. When compared against radar-derived rain rates, the 1 km domain produces superior CSI values for all rain rates. The daily-average hourly Fractional Skill Score (FSS) was then calculated for some dates and showed agreement with the CSI results with the exception of a north-east monsoon day where, for heavier rain rates, the 3 km domain has superior FSS. We also investigate a particularly heavy rain event on 10 January 2021 and show that the 3 km grid has highest CSI for rain rates of 4 mm/h and 16 mm/h (based on both ground-based and radar-derived rain rates), however, the 1 km has superior FSS for this event. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) models such as the Weather Research and Forecasting (WRF) model are increasingly used over the Indian region to forecast extreme rainfall events. However, studies which explore the application of high-resolution rainfall simulations obtained from the WRF model in urban hydrology are limited. In this paper, the utility of a model coupling framework to predict urban floods is explored through the case study of Bangalore city in India. This framework is used to simulate multiple extreme events that occurred over the city for the monsoons of years 2020 and 2021. To address the uncertainty from the WRF model, a 12-member convection permitting ensemble is used. Model configurations using Kain Fritsch and WSM6 parameterization schemes could simulate the spatial and temporal pattern of the selected event. The city is easily flooded with rainfall events above a threshold of 60 mm/day and to capture the response of the urban catchment, the Personal Computer Storm Water Management Model (PCSWMM) is used in this study. Flood forecasts are created using the outputs from the WRF ensemble and the Global Forecasting System (GFS). The high temporal and spatial resolution of the rainfall forecasts (

Precipitable water vapor (PWV) monitoring with real-time precise point positioning (PPP) is required for the improved early detection of increasingly common extreme weather occurrences. This study takes Hong Kong as the research object. The aim is to explore the accuracy of real-time global navigation satellite system (GNSS) PPP in estimating PWV at low latitudes and under different weather conditions. In this paper, real-time PPP is realized by using observation data from continuously operating reference stations (CORS) in Hong Kong and real-time products from the Centre National d’Etudes Spatiales (CNES). The Tm model calculated using numerical weather prediction (NWP) data converts the zenith tropospheric delay (ZTD) of real-time PPP inversion into PWV and evaluates its accuracy using postprocessing products. The experimental results show that compared with GPS, multi-GNSS can reduce the convergence time of PPP by 29.20% during rainfall periods and by 12.06% during nonrainfall periods. The improvement in positioning accuracy is not obvious, and the positioning accuracy of the two is equivalent. Real-time PPP ZTD experiments show that there are lower average values for bias, standard deviation (STDEV), and root mean square (RMS) during nonrainfall periods than during rainfall periods. Real-time PPP PWV experiments show that there are also lower bias, STDEV, and RMS values during nonrainfall periods than during rainfall periods. The comparative study between rainfall and nonrainfall periods is of great significance for the real-time monitoring and forecasting of water vapor changes.

Forecasts of numerical weather prediction models unavoidably contain errors, and it is a common practice to post-process the model output and correct the error for the proper use of the forecasts. This study develops a grid-to-multipoint (G2N) model output error correction scheme which extracts model spatial features and corrects multistation forecasts simultaneously. The model was tested for an operational high-resolution model system, the precision rapid update forecasting system (PRUFS) model, running for East China at 3 km grid intervals. The variables studied include 2 m temperature, 2 m relative humidity, and 10 m wind speed at 311 standard ground-based weather stations. The dataset for training G2N is a year of historical PRUFS model outputs and the surface observations of the same period and the assessment of the G2N performance are based on the output of two months of real-time G2N. The verification of the real-time results shows that G2N reduced RMSEs of the 2 m temperature, 2 m relative humidity, and 10 m wind speed forecast errors of the PRUFS model by 19%, 24%, and 42%, respectively. Sensitivity analysis reveals that increasing the number of the target stations for simultaneous correction helps to improve the model performance and reduces the computational cost as well indicating that enhancing the loss function with spatial regional meteorological structure is helpful. On the other hand, adequately selecting the size of influencing grid areas of the model input is also important for G2N to incorporate enough spatial features of model forecasts but not to include the information from the grids far from the correcting areas. G2N is a highly efficient and effective tool that can be readily implemented for real-time regional NWP models.

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51-331 km) is lower than 2020 (82-359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76-332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin. [ABSTRACT FROM AUTHOR]

Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone‐related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large‐eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long‐term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially‐averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to street level, given pre‐computed scenarios of atmospheric conditions. For Helsinki, the observed winds are stronger during winter than summer, and, on average, higher wind speeds were observed at the urban site than the sub‐urban site. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) has revolutionized activities involving geodetic positioning. To achieve a desired accuracy, it is essential to model the atmosphere in an appropriate way. With respect to the neutral atmosphere, the signal sent by the satellite suffers a delay when crossing this layer during its travel to the receiver on the surface, the so-called neutral atmospheric delay. Although empirical models exist, they may not be suitable to represent microclimatic variations in different regions of the globe due to peculiarities that exist in diverse areas. To minimize this limitation, correction models based on numerical weather prediction (NWP) emerge. They allow the assessment of the delay from local atmospheric parameters and the evaluation of atmospheric particularities of each region. In addition, another way to obtain neutral atmosphere delay is by making use of data from radiosondes, which measure atmospheric data at various altitude levels. The main objective of this article is to investigate the performance of different models using GNSS data collected in countries with different climatic conditions. Assessment is performed on the positioning domain using the precise point positioning (PPP) technique. The results show that the proximity between the NWP-based models and radiosondes was approximately 3 cm, and that between empirical models was 5 cm, with variations that depended on the model and the region. Regarding the impact on the height component, the difference between the accuracy of the empirical and NWP models was approximately 16 cm. [ABSTRACT FROM AUTHOR]

Developments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two-part review article. This part, Part II, reviews the progress in recent years, from about 2000. It includes summaries of advances in the relevant satellite remote-sensing technologies and in methods to assimilate observations from these instruments into NWP systems. It also summarises impacts on forecast skill. Continued progress has been made on the assimilation of passive infrared (IR) sounding data and microwave (MW) sounding and imaging data. This has included data from hyperspectral IR sounders, which first became available during this period. Advances in the use of cloud-affected radiances, from both IR and MW instruments, have been made. In support of this progress, further developments have been made in fast radiative transfer models and in bias correction techniques, and work has continued to improve understanding and representation of observation uncertainties. Continued progress has also been made on the use of wind information from satellites, including atmospheric motion vectors and scatterometer data. A new source of temperature and humidity information, from radio occultation observations, has become available during the period and has been exploited by many NWP centres. The impact of satellite data on NWP accuracy is continually assessed using a range of methods and metrics. Some results from recent Observing System Experiments (OSEs) and Forecast Sensitivity to Observation Impact (FSOI) assessment are presented and other methods are discussed. The role of satellite data in NWP-based atmospheric reanalysis systems is also described. [ABSTRACT FROM AUTHOR]

• Direct normal irradiance (DNI) is computed by a physics-based solution of the gross solar radiation in the circumsolar region. • The new DNI model is coupled with a numerical weather prediction model to forecast day-ahead DNI over the contiguous United States. • The significant bias in the conventional forecasting models has been remarkably reduced. Direct normal irradiance (DNI) is often interpreted differently in ground measurements and forecasts using numerical weather prediction (NWP) models, leading to substantial bias in DNI forecasting especially under cloudy-sky conditions. To mitigate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to conduct physics-based simulations of solar radiation in the circumsolar region. The DNI is quantified using the sum of the radiation along the sun direction and the scattered radiation within the circumsolar region. FARMS-DNI is coupled with the Weather Research and Forecasting model with solar extensions (WRF-Solar) to predict day-ahead DNI in 9-km pixels over an extended domain covering the contiguous United States. The DNI forecasts for the entire year of 2018 are validated using surface-based observations at the Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) sites and a satellite-based solar radiation product developed by the procedure of the National Solar Radiation Data Base (NSRDB). The results show that the WRF-Solar coupled with the conventional Beer-Bouguer-Lambert law underestimes the overall DNI forecasts by 30%-60%, which is a joint effect of the uncertainties in computing the circumsolar radiation and forecasting cloud properties. This bias is significantly reduced by using FARMS-DNI. [ABSTRACT FROM AUTHOR]

Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts. Plain Language Summary: This study investigated the heatwave (HW) and urban heat island (UHI) impacts on urban/rural temperature using different numerical models and test the accuracy of applying different model configurations. Model performances are evaluated based on ground‐based observations. Impacts of HW and UHI were quantified with verified model simulation. The potential health risk was estimated combined with historical data. This study is helpful for the urban manager to investigate the heat impacts on the urban population and also provide epidemiologists evidence to investigate the urban/rural population exposure to heat‐related health issues. Key Points: Extreme heatwave induced ∼4°C increase in temperature in rural Chicago while increased urban temperature by 2–3°CUrban heat island intensity is estimated to be around 1.44–2.38°CExcessive Heat Factor is higher than 50°C2 in urban Chicago due to heatwave [ABSTRACT FROM AUTHOR]

Abstract Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone‐related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large‐eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long‐term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially‐averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to street level, given pre‐computed scenarios of atmospheric conditions. For Helsinki, the observed winds are stronger during winter than summer, and, on average, higher wind speeds were observed at the urban site than the sub‐urban site.

Abstract Accelerated urbanization increases both the frequency and intensity of heatwaves (HW) and urban heat islands (UHIs). An extreme HW event occurred in 2012 summer that caused temperatures of more than 40°C in Chicago, Illinois, USA, which is a highly urbanized city impacted by UHIs. In this study, multiple numerical models, including the High Resolution Land Data Assimilation System (HRLDAS) and Weather Research and Forecasting (WRF) model, were used to simulate the HW and UHI, and their performance was evaluated. In addition, sensitivity testing of three different WRF configurations was done to determine the impact of increasing model complexity in simulating urban meteorology. Model performances were evaluated based on the statistical performance metrics, the application of a multi‐layer urban canopy model (MLUCM) helps WRF to provide the best performance in this study. HW caused rural temperatures to increase by ∼4°C, whereas urban Chicago had lower magnitude increases from the HW (∼2–3°C increases). Nighttime UHI intensity (UHII) ranged from 1.44 to 2.83°C during the study period. Spatiotemporal temperature fields were used to estimate the potential heat‐related exposure and to quantify the Excessive Heat Factor (EHF). The EHF during the HW episode provides a risk map indicating that while urban Chicago had higher heat‐related stress during this event, the rural area also had high risk, especially during nighttime in central Illinois. This study provides a reliable method to estimate spatiotemporal exposures for future studies of heat‐related health impacts.

This paper proposes a simple and powerful optimal integration (OPI) method for improving hourly quantitative precipitation forecasts (QPFs, 0-24 h) of a single-model by integrating the benefits of different biascorrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration (CMA). Three techniques are used to generate multi-method calibrated members for OPI: deep neural network (DNN), frequency-matching (FM), and optimal threat score (OTS). The results are as follows: (1) The QPF using DNN follows the basic physical patterns of CMA-GD. Despite providing superior improvements for clear-rainy and weak precipitation, DNN cannot improve the predictions for severe precipitation, while OTS can significantly strengthen these predictions. As a result, DNN and OTS are the optimal members to be incorporated into OPI. (2) Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation. Compared with the CMA-GD, OPI improves the TS by 2.5%, 5.4%, 7.8%, 8.3%, and 6.1% for QPFs from clear-rainy to rainstorms in the verification dataset. Moreover, OPI shows good stability in the test dataset. (3) It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation. Therefore, improvements in predicting severe precipitation using this method should be further realized by improving the numerical model's forecasting capability. [ABSTRACT FROM AUTHOR]

Extreme rainfall events have posed several serious threats to many populated and urbanized areas in the world including the Indian subcontinent. Therefore, accurate predictions of their intensity and areas of influence are important for flood‐prone risk assessments. On 14 July 2016, heavy to exceptionally heavy rainfall occurred in Yamunanagar (30.16° N, 77.29° E), located in the state of Haryana in North India, which led to widespread disruption of communication, electricity, inundation of houses, and so forth. The present paper aims at examining observational, synoptic, thermo‐dynamical, and numerical features associated with this devastating rainfall episode. The analysis found that during extreme rainfall episodes, a trough in mid‐tropospheric westerlies and a strong low‐level atmospheric monsoonal flow seem to have strongly interacted with each other, creating a strong convergence zone near study areas that led to a severe rainstorm. The quasi‐stationary supercells were also noticed due to continuous moisture incursions from the Bay of Bengal and orographic uplift over the Himalayas near Yamunanagar. A deep layer of wind shear interacts dynamically with the convergence zone and leads to a potential rainstorm. Thermodynamic indices indicate high instability over the heavy rainfall area. The dynamics of this event were studied in detail by using three‐dimensional variational data assimilation within the weather research and forecasting model, configured with triple two‐way nesting domains (27, 9, and 3 km). The model results show that the weather research and forecasting model satisfactorily captures the quantitative precipitation (300 mm) in 24 h over the Yamunanagar region as compared with observation (365 mm). [ABSTRACT FROM AUTHOR]

A transformação digital é uma realidade em todas as áreas da atividade humana e vem sendo acelerada pela recente pandemia da Covid-19. O uso de Inteligência Artificial, Data Analytics, Realidade Aumentada, Robots, dentre outras tecnologias viabiliza a automação de processos praticamente em todos os segmentos da economia: agricultura, manufatura e serviços. Entender os fenômenos climáticos e seus impactos no ambiente sempre foi um desafio para o homem que, desde a antiguidade e até os dias atuais, se depara com situações não previstas, que trazem impacto em seu dia a dia, colocando, muitas vezes, a própria vida em risco. Na atualidade, além de buscar entender, o que se busca é prever o clima e seus impactos ambientais, de modo a mitigar os riscos, viabilizar a continuidade do desenvolvimento, preservando a natureza (fauna, flora, recursos naturais etc). O uso de ferramentas computacionais para modelagem numérica que representem este fenômenos é de suma importância, endereçando a necessidade de previsões cada vez mais rápidas e confiáveis. Este projeto analisa ferramentas disponíveis no mercado, que utilizam três modelos numéricos distintos, descrevendo as funcionalidades existentes e a assertividade na previsão de fenômenos climáticos em Santa Catarina

Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations are limited to frequent flight routes, and vertical profiles are provided in the vicinity of large cities. However, there are large areas with few radiosonde launches, in particular above the oceans and in the polar areas. In this article, the development and technical details of the unmanned aerial system LUCA (Lightweight Unmanned high Ceiling Aerial system) are described. LUCA has the potential to complement radiosonde and aircraft-based observations up to 10 km in altitude. The system ascends and descends (by electrical power) in spiral trajectories and returns to the launching site. This article discusses the requirements for obtaining high data availability under mid-European and Antarctic conditions, with highly automated take-offs and landings under high surface winds, the capacity to deal with icing, and the ability to operate under high wind speeds. The article presents technical solutions for the design and construction of the system and demonstrates its potential.

The multi-model ensemble (MME) forecast for meteorological elements has been proved many times to be more skillful than the single model. It improves the forecast quality by integrating multiple sets of numerical forecast results with different spatial-temporal characteristics. Currently, the main numerical forecast results present a grid structure formed by longitude and latitude lines in space and a special two-dimensional time structure in time, namely the initial time and the lead time, compared with the traditional one-dimensional time. These characteristics mean that many MME methods have limitations in further improving forecast quality. Focusing on this problem, we propose a deep MME forecast method that suits the special structure. At spatial level, our model uses window self-attention and shifted window attention to aggregate information. At temporal level, we propose a recurrent like neural network with rolling structure (Roll-RLNN) which is more suitable for two-dimensional time structure that widely exists in the institutions of numerical weather prediction (NWP) with running service. In this paper, we test the MME forecast for sea level pressure as the forecast characteristics of the essential meteorological element vary clearly across institutions, and the results show that our model structure is effective and can make significant forecast improvements.

In this study, near-neutrally stratified planetary boundary layer (PBL) flows over complex terrain are numerically investigated via a one-way coupling of mesoscale numerical weather prediction (NWP) with unsteady computational fluid dynamics (CFD) codes. First, empirical turbulence model constants are modified to accomplish consistent turbulent viscosities between PBL schemes of mesoscale NWP and the k - ω turbulence closure of microscale CFD. Next, to reconstruct turbulent inflows calculated from coarse NWP analysis, inverse distance weighted (IDW) interpolation is optimized and adopted around the coupling interfaces. This method has been shown to decrease prediction uncertainty, as high-aspect-ratio NWP grids are used. Compared with time-series measurements, the proposed approach presents wind velocities in agreement around both upstream and separated regions in the Askervein Hill case, although the turbulent kinetic energy on the leeward side is still underestimated. Another moderately complex terrain with an enlarged domain around Feng Men Hill also exhibits improved numerical results. Moreover, the unsteady simulations reveal that both wind speed-up and wake are highly correlated with inflow characteristics. The successful integration of the nesting simulation with a general CFD code demonstrates the flexibility and efficiency of this procedure in modeling high-resolution unsteady flows over complex terrain. [ABSTRACT FROM AUTHOR]

In the framework of the EUMETSAT Polar System–Second Generation (EPS‐SG) preparation, a new generation of the Infrared Atmospheric Sounding Interferometer (IASI) instrument has been designed. The IASI‐New Generation (IASI‐NG) will measure radiances at a doubled spectral resolution compared to its predecessor and with a signal‐to‐noise ratio improved by a factor of 2. The large amount of data arising from IASI‐NG will present many challenges for data transmission, storage and assimilation. Moreover, the full set of measured radiances will not be exploitable in an operational numerical weather prediction (NWP) context. For these reasons, an appropriate IASI‐NG channel selection in needed, aiming to select the most informative channels for NWP models. Therefore, the standard iterative channel selection methodology, based on the optimal linear estimation theory and assuming spectrally correlated errors, has been applied to a set of simulated data of the IASI‐NG spectrum. The entire simulated IASI‐NG spectrum has been first investigated, while finally focusing the channel selection procedure on the most interesting wavelength ranges for the assimilation. Through this process, a total of 500 channels have been chosen to serve as a basis for future channel selections to be provided to NWP centres – 277 temperature, 23 surface‐sensitive and 200 water vapour channels. One‐dimensional variational (1D‐Var) assimilation experiments show that using this selected set of channels leads to a reduction of the standard deviation of the error in temperature (up to 30%) and water vapour (up to 50%) profiles with respect to the apriori information. [ABSTRACT FROM AUTHOR]

Two methods are widely used to assess the impact of observations in global numerical weather prediction (NWP): data denial experiments (DDEs) and the forecast sensitivity‐based observation impact (FSOI) method. A DDE measures the impact on forecast accuracy of removing an observation type from the system, whereas FSOI measures the amount by which an observation type reduces the short‐range forecast error (within a system containing all observation types). It is usually found, in global NWP experiments in which both methods are used, that the FSOI impact of a given observation type is greater than its DDE impact. The aim of this article is to present the theory behind the DDE and FSOI metrics that are commonly used and to explore factors that cause DDE and FSOI metrics to give different results. The general theory is presented for an optimal analysis and forecast system and then applied to a simple model with two state variables. To explain the commonly found result of FSOI impact greater than DDE impact, the following system properties are found to be important: mixing of information (and error) by the forecast model between state variables, the rate of forecast error growth, the presence of forecast model error, the way in which observational information is distributed between state variables and denied from them, and the presence of error in the data used for forecast verification. These results provide insight into why NWP systems are resilient to the removal of observational information; they are shown to be resilient if information is denied primarily from well‐observed variables but not when denied primarily from poorly observed variables. [ABSTRACT FROM AUTHOR]

The philosophy of forecast verification is rather different between deterministic and probabilistic verification metrics: generally speaking, deterministic metrics measure differences, whereas probabilistic metrics assess reliability and sharpness of predictive distributions. This article considers the root‐mean‐square error (RMSE), which can be seen as a deterministic metric, and the probabilistic metric Continuous Ranked Probability Score (CRPS), and demonstrates that under certain conditions, the CRPS can be mathematically expressed in terms of the RMSE when these metrics are aggregated. One of the required conditions is the normality of distributions. The other condition is that, while the forecast ensemble need not be calibrated, any bias or over/underdispersion cannot depend on the forecast distribution itself. Under these conditions, the CRPS is a fraction of the RMSE, and this fraction depends only on the heteroscedasticity of the ensemble spread and the measures of calibration. The derived CRPS–RMSE relationship for the case of perfect ensemble reliability is tested on simulations of idealised two‐dimensional barotropic turbulence. Results suggest that the relationship holds approximately despite the normality condition not being met. [ABSTRACT FROM AUTHOR]