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Auroral particle precipitation is usually assumed to be equally strong for both signs of the By component of the interplanetary magnetic field (IMF). This is also the case in most currently used precipitation models, which parameterize solar wind driving by empirical coupling functions. However, recent studies have showed that geomagnetic activity is significantly modulated by the signs and amplitudes of IMF By and the Earth's dipole tilt angle Ψ. This so called explicit By dependence is not yet included in any current precipitation models. In this paper, we quantify this By dependence for auroral electron precipitation for the first time. We use precipitation measurements of the Defense Meteorological Satellite Program (DMSP) Special Sensor J instruments from years 1995–2022. We show that the dawnside electron precipitation at energies 13.9–30 keV is greater at auroral latitudes for opposite signs of By and Ψ in both hemispheres, while the dusk sector is mostly unaffected by By and Ψ. For energies below 6.5 keV the By dependence is strong poleward of the auroral oval in the summer hemisphere, also exhibiting a strong dawn‐dusk asymmetry. We also show that By dependence of precipitation modulates ionospheric conductance. Plain Language Summary: Electron precipitation into the Earth's ionosphere creating the aurora borealis is driven by the magnetic field carried by the solar wind. The electron precipitation is known to increase the most, when the magnetic field of the solar wind points to south. The currently used models assume equally strong electron precipitation for eastward and westward solar wind magnetic fields. However, in this paper we show that the electron precipitation is different for duskward and dawnward magnetic fields, and that this magnetic field dependence varies with the season. We show that the electron precipitation is stronger on the dawnside in both hemispheres during the northern hemisphere winter when the solar wind magnetic field points duskward. In northern hemisphere summer the dependence on the magnetic field direction is opposite. We show that the solar wind magnetic field modulates the ionospheric conductivity in a similar way. Key Points: IMF By and Earth's dipole tilt angle Ψ (season) modulate auroral electron precipitationPrecipitation is stronger for 13.9–30 keV at auroral latitudes on the dawnside for both hemispheres, when By and Ψ have opposite signsBy drives a strong dawn‐dusk difference for energies 1.4–6.5 keV in the summer hemisphere [ABSTRACT FROM AUTHOR]

Accurate precipitation measurements are essential for understanding hydrological processes in high-altitude regions. Conventional gauge measurements often yield large underestimations of actual precipitation, prompting the development of statistical methods to correct the measurement bias. However, the complex conditions at high altitudes pose additional challenges to the statistical methods. To improve the correction of precipitation measurements in high-altitude areas, we selected the Yakou station, situated at an altitude of 4147 m on the Tibetan plateau, as the study site. In this study, we employed the machine learning method XGBoost regression to correct precipitation measurements using meteorological variables and remote sensing data, including Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Additionally, we examined the transferability of this method between different stations in our study site, Norway, and the United States. Our results show that the Yakou station experiences a large underestimation of precipitation, with a magnitude of 51.4%. This is significantly higher than similar measurements taken in the Arctic or lower altitudes. Furthermore, the remote sensing precipitation datasets underestimated precipitation when compared to the Double Fence Intercomparison Reference (DFIR) precipitation observation. Our findings suggest that the machine learning method outperformed the traditional statistical method in accuracy metrics and frequency distribution. Introducing remote sensing data, especially the GSMaP precipitation, could potentially replace the role of in situ wind speed in precipitation correction, highlighting the potential of remote sensing data for correcting precipitation rather than in situ meteorological observation. Moreover, our results indicate that the machine learning method with remote sensing data demonstrated better transferability than the traditional statistical method when we cross-validated the method with sites located in different countries. This study offers a promising strategy for obtaining more accurate precipitation measurements in high-altitude regions. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is a global climate phenomenon recognised for its effects on regional climates and irregularities in climate variability worldwide. In this article, the impact of ENSO on rainfall anomalies in Central Java, Indonesia, was investigated from 1990 to 2019. The Oceanic Niño Index (ONI) was used to determine the ENSO phase, and rainfall estimates by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and rainfall records from several meteorological stations were used to identify the anomalies. The anomalies were explained in relation to the degree of strength of El Niño and La Niña events. The results showed that strong El Niño caused a decrease in annual rainfall intensities to the entire region, particularly in the mountainous area in the western part (Pemalang, Pekalongan, Purbalingga, Banjarnegara) and northeast part (Jepara, Kudus, Pati). The decrease in annual rainfall reached 1300 mm during strong El Niño. The moderate and weak El Niño also affect those areas, while the mountainous area in the west part and southwest part is not affected. The moderate and weak El Niño contributes to decreasing rainfall of up to 900 mm, with notable differences in the affected area. Strong La Niña affects the entire study area, with increasing annual rainfall up to 1700 mm, while moderate and weak La Niña less affects the region, and a notable difference was not identified. The result also shows that the spatial distribution of this decrease in rainfall is across the northern regions and became increasingly less towards the south during strong El Niño. Conversely, strong La Niña increased the rainfall intensity, starting from the south and weakening northward. The effect enhanced significantly with La Niña's strength, especially on the southern coast of the study area. [ABSTRACT FROM AUTHOR]

Knuth et al. (2010) used snow height measurements from sonic ranging sensors installed at automatic weather station (AWS) sites on the Ross Ice Shelf to track changes in snow height with the corresponding meteorological measurements to investigate precipitation and horizontal snow transport in terms of surface accumulation. Observations from the Pluvio HT ht precipitation gauge indicate that in mid-March 2018 the snow surface reached a height at which the drifting snow was able to enter the opening and fill the precipitation gauge. Eisen et al. (2008) cover a range of ground-based measurements of snow accumulation, including stakes, ultrasonic sounders, snow pits, and firn and ice cores, to provide an understanding of snow accumulation over East Antarctica. It is challenging to measure precipitation in Antarctica due to the relatively small amount of precipitation (Palerme et al., 2014), the difficulty in distinguishing between falling snow (precipitation) and blowing snow (Souverinjns et al., 2018), and the need to do all of this in a remote environment requiring a low-power and autonomous measurement system. [Extracted from the article]

The European drought of 1921 is assessed in terms of its impacts on society and in terms of its physical characteristics. The development of impacts of the drought are categorized by a systematic survey of newspaper reports from five European newspapers covering the area from England to the Czech Republic and other parts of Europe. This is coupled to a reconstruction of daily temperature and precipitation based on meteorological measurements to quantify the drought severity and extent, and reanalysis data are used to identify its drivers. This analysis shows that the first impacts of the drought started to appear in early spring and lingered on until well into autumn and winter, affecting water supply and agriculture and livestock farming. The dominant impact in western Europe is on agriculture and livestock farming while in central Europe the effects of wildfires were reported on most often. The peak in the number of reports is in late summer. Preceding the first impacts was the dry autumn of 1920 and winter 1920–1921. The area hardest hit by the drought in the following spring and summer was the triangle between Brussels, Paris and Lyon, but a vast stretch of the continent, from Ireland to the Ukraine, was affected. The reported impacts on water supply and water-borne transport in that region were matched by an analysis of the hydrological situation over the Seine catchment. On average, the 1921 summer was not particularly hot, but the heatwave which was observed at the end of July saw temperatures matching those of the heatwaves in modern summers. Similar to modern droughts, an anticyclone was present roughly over the British Isles, maintaining sunny and dry weather in Europe and steering away cyclones to the north. Its persistence makes it exceptional in comparison to modern droughts. The 1921 drought stands out as the most severe and most widespread drought in Europe since the start of the 20th century. The precipitation deficit in all seasons was large, but in none of the seasons in 1920 and 1921 was the precipitation deficit the largest on record. The severity of the 1921 drought relates to the conservative nature of drought which amplifies the lack of precipitation in autumn and winter into the following spring and summer. [ABSTRACT FROM AUTHOR]

A precipitation observation network was constructed in a high-altitude area of the Qilian Mountains in northwest China, which contained 23 sets of instruments. Because 21 sets of instruments were surrounded by protective fences, a precipitation intercomparison experiment was carried out at the highest station (4651m) of the observation network with the same configuration to study the impact of wind on precipitation measurements under this configuration and to develop a correction method suitable for the entire network. The 30-min measured precipitation from June 2018 to October 2019 was corrected by the World Meteorological Organization Solid Precipitation Intercomparison Experiment transfer functions, and their parameters were recalibrated with a local dataset. The results showed that the transfer functions fitted to the local dataset had a better performance than those using original parameters. Because of the influence of the experimental configuration on wind speed and direction, the root-mean-square error (RMSE) corrected with the original parameters increased by an average of 86%, but the RMSE adjusted by the new transfer functions decreased by 9%.Moreover, the resultant biases using new transfer functions were close to 0. The new coefficients of snowfall were derived by local measurements and applied to datasets from other sites within the Qilian Mountains observation network to evaluate their performances. The corrected snowfall at 21 stations increased by an average of 32.6mm, and the relative precipitation increment ranged from 6% to 26%. This method can be used to correct snowfall in the observation network under the nonstandard site configuration. [ABSTRACT FROM AUTHOR]

The diurnal cycle of precipitation is important to the hydroclimate in the Himalayas in summer; however, features of the diurnal cycle that affect precipitation from the foothills to glacierized, high‐elevation areas are poorly understood. We investigated the diurnal cycle of precipitation using 3 years of in situ observations recorded close to a glacier at 4,806 m asl, and 17 years of data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Integrated Multi‐satellitE Retrieval for Global Precipitation Measurement (IMERG). The mechanisms that drive the diurnal cycle were examined using hourly European Center for Medium‐Range Weather Forecasts Re‐Analysis fifth generation (ERA5) data. In situ observations showed that the diurnal precipitation cycle has daytime and nighttime peaks, which both consist of high rainfall frequency and low rainfall intensity. In addition, twice‐daily maxima exist in the TRMM PR data, particularly over two rain bands at around 500–1,000 m asl, and at ∼2,000 m asl. Convective‐type rainfall, with a lower rain‐top height, occurs in the daytime, whereas stratiform‐type rain, with a higher rain‐top height, occurs at night, particularly over terrain at elevations above ∼1,500 m asl. Land surface processes likely cause the two peaks in the diurnal cycle. Daytime surface heating drives upslope flows that promote condensation. At night, surface cooling over the plain to the south of the Himalayas causes low‐level monsoon flows to accelerate, creating a nocturnal jet, which results in large‐scale moisture flux convergence over the southern slopes. Plain Language Summary: Orographically induced precipitation and meltwater from glaciers in the Himalayas feeds into the headwaters of major rivers such as the Indus, the Ganges, and the Brahmaputra, and provides water resources for the large population of South Asia. Glaciers from the central to the eastern Himalayas are sustained by summer precipitation, and the diurnal cycle is an important part of the climate system there. However, the features of the diurnal cycle, which stretches from the foothills to the glacierized high‐elevation area in the Nepal Himalayas, are poorly understood. In this study, we investigated the diurnal cycle of precipitation in summer using data from 3 years of in situ observations (recorded close to a glacier at 4,806 m asl) and 17 years of space‐borne precipitation radar observations. We identified twice‐daily precipitation maxima, corresponding to daytime and nighttime peaks, in the diurnal cycle across much of the Nepal Himalayas. The two maxima are the result of different land surface effects. The daytime peak results from upslope flows that are driven by surface heating of the slopes, whereas the nighttime peak is caused by the acceleration of low‐level monsoon flows toward the Himalayas as the surface cools over the plain to the south of the Himalayas. Key Points: Twice‐daily precipitation maxima, corresponding to day and night, occur widely across the Nepal Himalayas, including in the glacierized areaA double band of rainfall that has twice‐daily maxima exists over terrain at 500–1,000 and ∼2,000 m asl on the southern slopesUpslope flows due to surface heating drive a daytime rainfall peak, and a monsoon nocturnal low‐level jet likely creates a nighttime peak [ABSTRACT FROM AUTHOR]

Droughts are complex natural phenomena that influence society's development in different aspects; therefore, monitoring their behavior and future trends is a useful task to assist the management of natural resources. In addition, the use of satellite-estimated rainfall data emerges as a promising tool to monitor these phenomena in large spatial domains. The Tropical Rainfall Measuring Mission (TRMM) products have been validated in several studies and stand out among the available products. Therefore, this work seeks to evaluate TRMM-estimated rainfall data's performance for monitoring the behavior and spatiotemporal trends of meteorological droughts over Paraíba State, based on the standardized precipitation index (SPI) from 1998 to 2017. Then, 78 rain gauge-measured and 187 TRMM-estimated rainfall time series were used, and trends of drought behavior, duration, and severity at eight time scales were evaluated using the Mann–Kendall and Sen tests. The results show that the TRMM-estimated rainfall data accurately captured the pattern of recent extreme rainfall events that occurred over Paraíba State. Drought events tend to be drier, longer-lasting, and more severe in most of the state. The greatest inconsistencies between the results obtained from rain gauge-measured and TRMM-estimated rainfall data are concentrated in the area closest to the coast. Furthermore, long-term drought trends are more pronounced than short-term drought, and the TRMM-estimated rainfall data correctly identified this pattern. Thus, TRMM-estimated rainfall data are a valuable source of data for identifying drought behavior and trends over much of the region. [ABSTRACT FROM AUTHOR]

Forecasting the combined power generated from intermittent solar plus wind capacity hourly on a country-wide basis from underlying environmental and market variables poses challenges. An optimized data matching algorithm demonstrates its capabilities to do this with meaningful accuracy. For data recorded for the year 2016 matched with hourly records from year 2015, it achieves prediction accuracy of RMSE = 895 MW and R2 = 0.9967. It does so by exploiting information from underlying variables, some of which are quite well correlated with the quantity of renewable power generation (e.g. wind speed and solar irradiance) and others that are very poorly correlated (e.g. quantity of precipitation, snow depth, cloud cover), without involving correlation calculations. The algorithm is also configured to provide ongoing short-term forecast, 1 h (t + 1) to 6 h (t + 6) ahead, throughout 2016 using historical data for different durations. For t + 1 forecasts across that year using a single tuned solution, it achieves forecasting accuracy of RMSE ~ 2100 MW; R2 ~ 0.97. Comparisons with the forecasts of an autoregressive integrated moving average (ARIMA) model for January 2016 indicate that TOB is outperformed in forecasting accuracy for the t + 1 to t + 11 h ahead but that TOB outperforms ARIMA in forecast accuracy for t + 13 to t + 30 h ahead. This suggests that the two models have the potential to complement each other in day-ahead forecasting. Potential remains to further improve TOB's forecasting accuracy by retuning the algorithm's optimization on a regular basis, e.g. monthly or quarterly, enlarging the historic dataset used for matching and applying adjustments for capacity growth. The promising results justify multi-year studies as data becomes available for subsequent years. [ABSTRACT FROM AUTHOR]

Remote sensing and hydrological models are one of the foremost tools for rapid and comprehensive study of flood hazards and disasters in any parts of the world. Current study is focused on severe 2018 Kerala flood, and is done using various remote sensing data, geospatial tools and combination of hydrological/hydrodynamic/topographical models. Flood mapping is done with pre and post floods remote sensing datasets. For pre-Flood analysis, Normalized Difference Water Index (NDWI) map was prepared on Google Earth Engine (GEE), using Sentinel-2 images for the period of Feb. 2017 to identify permanent water bodies. For post-Flood analysis, GEE was used to download the pre-processed and thermal noise removed Sentinel-1 SAR image for Aug. 9, 2018, Aug. 14 and Aug. 21, 2018 and flood maps were generated using this data. In addition to SAR data, probable flood inundation areas using topography-based flood inundation tool HAND (Height Above Nearest Drainage tool) was also utilized. Hydrological simulation was carried out for all 12 major river sub-basins of Kerala, where floods are reported. Indian Meteorological Department-Global Precipitation Measurement (IMD-GPM) gridded daily data is used as input meteorological data for hydrological simulations. The hydrological simulations results were verified using published Central Water Commission (CWC) reports and reservoirs data for India-WRIS. The hydrodynamic simulation was also performed for simulating the Idukki dam release data and flood condition in downstream areas. Overall, an integrated study and developed approach can be utilized by state and central water and disaster management agencies to develop flood early warning systems. [ABSTRACT FROM AUTHOR]

The unconditioned data retrieved from accumulating automated weighing precipitation gauges are inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: (1) the operational 15 min filter (O15), (2) the neutral aggregating filter (NAF), (3) the supervised neutral aggregating filter (NAF-S), and (4) the segmented neutral aggregating filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation primarily focuses on cold-season (October to April) accumulating automated weighing precipitation gauge data at 1 min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift and raw (minimally processed) data from several WMO Solid Precipitation Intercomparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99 %, 100 %, and 102 % of the control total precipitation for low-, medium-, and high-noise scenarios respectively for the cold-season (October–April) and 97 % of the control total precipitation for all noise scenarios in the warm season (May–September). Among the filters, the fully automated NAF-SEG produced the highest correlation coefficients and lowest root-mean-square error (RMSE) for all synthetic noise levels, with comparable performance to the supervised and manually intensive NAF-S method. Compared to the O15 method in cold-season testing, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. In warm-season testing, the NAF-SEG bias was lower or similar in 7 of 11 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24 h latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in combination with a simple real-time filter, such as the O15 or similar filter. [ABSTRACT FROM AUTHOR]

This study investigates the effects of microphysical processes on the precipitation spectrum in a strongly forced environment using the vector vorticity cloud‐resolving model (VVM). Experiments are performed under imposed advective cooling and moistening with two microphysics parameterizations: the predicted particle properties scheme (P3) and Lin scheme (VVM‐Lin). Even though the domain‐averaged precipitation is similar in the two experiments, P3 exhibits stronger extreme precipitation in the spectrum compared with VVM‐Lin. Changes in convective structures are responsible for such a difference. Using the isentropic analyses, we identify that in P3, stronger convective updrafts take place in the high θei regime, where air parcels rarely reach. This is caused by the reduced melting of rimed ice particles for energic parcels. Through defining convective core clouds, the relation between the convective structure on the isentropic diagram and the extreme precipitation can be identified. The shifts toward extreme intensity in the precipitation spectrum suggest that the microphysical processes have significant impacts on the extreme precipitation by the convective core clouds. The treatment of microphysics has significant impacts on the convective structures and then alter the probability of extreme events under the strongly forced environment. Plain Language Summary: The microphysical parameterization typically represents cloud microphysical processes in the numerical models. In this study, the authors implemented a new parameterization (P3) in the model. The results show that extreme precipitation is more likely to occur when the environment is warm and moist compared with the original microphysics scheme. The change of the extreme precipitation is associated with the reduced melting effect in an ascending parcel. The melting of ice particles weakens the upward velocity of the parcel and then reduce the extreme precipitation. P3 can reduce the melting effect by its ability to represent fast‐falling hail particles, which can leave the parcel rapidly. Key Points: The predicted particle properties (P3) microphysics scheme is formally implemented in a vector vorticity equation cloud‐resolving model (VVM)The P3 scheme exhibits stronger extreme precipitation under a strongly forced environment compared to the original schemeThe reduced melting effects associated with strong convective updrafts in P3 can result in more extreme precipitation [ABSTRACT FROM AUTHOR]

California depends on snow accumulation in the Sierra Nevada for its water supply. Snowfall is measured by a combination of snow pillows, snow courses, and rain gauges. However, the paucity of locations of these measurements, particularly at high elevations, can introduce artifacts into precipitation estimates that are detrimental for hydrologic forecasting. To reduce errors, we need high-resolution, spatially complete measurements of precipitation. Remotely sensed snow depth and snow water equivalent (SWE), with retrieval time scales that resolve storms, could help mitigate this problem in snow-dominated watersheds. Since 2013, National Aeronautics and Space Administration's Airborne Snow Observatory (ASO) has measured snow depth in the Tuolumne basin of California's Sierra Nevada to advance streamflow forecasting through improved estimates of SWE. In early April 2015, two flights 6 days apart bracketed a single storm. The work herein documents a new use for ASO and presents a methodology to directly measure the spatial variability of frozen precipitation. In an end-to-end analysis, we also compare gauge-interpolated and dynamically downscaled estimates of precipitation for the given storm with that of the ASO change in SWE. The work shows that the extension of ASO operations to additional storms could benefit our understanding of mountain hydrometeorology by delivering observations that can truly evaluate the spatial distribution of snowfall for both statistical and numerical models. Plain Language Summary Precipitation, which includes rain, snow, sleet, and hail, recycles water from the atmosphere back to Earth's surface. The dynamic way in which the atmosphere and land interact generates highly variable precipitation rates, particularly in mountain landscapes. To quantify precipitation, we currently use a network of gauges at fixed locations, and to make these measurements more meaningful, we use statistics or models to "fill the blanks." However, because the "blanks" often incorporate mountain peaks and valleys, it is challenging, if not impossible, to discern the accuracy and precision of our models and understanding of the process. Consequently, we require new ways of measuring mountain precipitation. Unlike rain, snow remains roughly in place following snowfall, thereby preserving the distribution of precipitation. Airborne lidar can now measure these changes in depth after storms at high accuracy and precision. Here we present a methodology that uses this technological advancement to map the variability in precipitation during a snowstorm. Ultimately, these techniques, if extended to additional storms in various regions around the world, could help to evaluate precipitation in weather models by improving understanding of mountain precipitation. [ABSTRACT FROM AUTHOR]

In operational conditions, wind is the main environmental source of measurement biases for catching-type precipitation gauges. The gauge geometry induces a deformation of the surrounding airflow pattern, which is generally characterized by relevant updraft zones in front of the collector and above it. This effect deviates the trajectories of the lighter hydrometeors away from the collector and thus is responsible for a significant reduction of the collection performance. Previous approaches to this problem, using computational fluid dynamics simulations and wind-tunnel tests, mostly assumed steady and uniform free-stream conditions. Wind is turbulent in nature, though. The role of natural free-stream turbulence on collection performance is investigated in this work for the case study of a calyx-shaped precipitation gauge and wind velocity between 10 and 18 m s−1. The unsteady Reynolds-averaged Navier–Stokes model was adopted. Turbulent conditions were simulated by imposing constant free-stream velocity and introducing a fixed solid fence upstream of the gauge to generate the desired turbulence intensity. Wind-tunnel measurements allowed validating numerical results by comparing measured and simulated velocity profiles in representative portions of the investigated domain. Results revealed that in the case of turbulent free-stream conditions both the normalized magnitude of the flow velocity and the updraft above the collector are reduced by approximately 20% and 12%, respectively. The dissipative effect of the turbulent fluctuations in the free stream has a damping role on the acceleration of the flow and on the updraft. This would result in a reduced undercatch with respect to literature simulations that employed the traditional uniform free-stream conditions. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is a key factor in the exchanging process of the hydrological cycle, which is rather difficult to be directly observed. In situ measurements on SM, however, are subjected to the point scales. Use of land surface models has been a promising way to explore SM variations, especially for poorly gauged high mountain regions such as the Yarlung Zangbo River (YZR) basin located in Southeast Tibetan Plateau. This study made an attempt to investigate the spatiotemporal variations of SM and discuss hydro‐meteorological factors impacting SM evolution based on the Global Land Data Assimilation Systems (GLDAS) outputs during the period 1970–2009. Results show that (a) GLDAS data sets have high agreement and low bias with in situ measurements and consistent spatial distribution with ERA reanalysis data sets; (b) an abrupt change of SM is detected in 1992 and a significantly decreasing trend happens during 1970–2009 and 1993–2009; (c) precipitation is the dominant climatic factor controlling SM during the period 1970–2009, whereas surface air temperature is the critical factor for the significant change of SM. Owing to the significant increasing of surface air temperature since 1992, its impact on SM increased by ∼91% than that before 1992. Evapotranspiration (ET) and snow water equivalent (SWE) are also taken into consideration, showing relatively weak influence on SM, which may be due to the dynamic process of coupled SM‐ET or low snow cover area fraction across the YZR basin. Findings in this study have important implications for SM variations in poorly gauged high mountain regions which may largely influences downstream water availability. [ABSTRACT FROM AUTHOR]

In orographic precipitation events, there are times when subsaturated low-level layers are observed to be below saturated, nearly moist-neutral, upper-level layers. By performing a series of idealized twodimensional simulations, this study investigates the response of orographic precipitation to subsaturated low-level layers. When the nondimensional parameter N2zt/U, where N2 and zt are, respectively, the dry Brunt--Väisälä frequency and depth of the subsaturated low-level layer, and U the cross-mountain wind speed, exceeds a critical value, the decelerated region on the upwind side of the mountain moves upwind, resulting in weak surface precipitation near the mountain peak. The critical value determined from the simulations is close to that derived from linear theory.When N2zt/Uis less than the critical value, increasing zt has two competing effects: 1) the vapor-transport effect,meaning that increasing zt decreases the amount of vapor transported to themountain, and hence tends to decrease surface precipitation; and 2) the updraftwidth effect, meaning that increasing zt enhances flow blocking, producing a wider updraft over the upwind slope, and hence tends to increase surface precipitation. When the vapor-transport effect dominates, surface precipitation decreases with zt. When the updraft-width effect dominates, surface precipitation increases with zt. Increasing the maximum mountain height hm or U generally increases surface precipitation. However, for certain combinations of hm and U, the simulations produce lee waves, which substantially reduce surface precipitation. Finally, the response of orographic precipitation in the simulations with both liquid-phase and ice-phase microphysics is similar to that in the simulations with only liquid-phase microphysics. [ABSTRACT FROM AUTHOR]

The unconditioned data retrieved from automated accumulating precipitation gauges is inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: 1) the operational 15-minute filter (O15), 2) the Neutral Aggregating Filter (NAF), 3) the Supervised Neutral Aggregating Filter (NAF-S), and 4) the Segmented Neutral Aggregating Filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation focuses on cold-season (October to April) accumulating-precipitation-gauge data at 1-min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift; and raw (minimally-processed) data from several WMO Solid Precipitation Inter-Comparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99%, 100%, and 102% of the control total precipitation for low, medium, and high noise scenarios respectively. Among the filters, the fully-automated NAF-SEG produced the highest correlation coefficients and lowest RMSE for all synthetic noise levels, with comparable performance to the supervised and manually-intensive NAF-S method. Compared to the operational O15 method, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24-hour latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in consort with a simple real-time filter, such as the operational O15 or similar filter. [ABSTRACT FROM AUTHOR]

This paper describes the experience of using weather radar data to simulate a catastrophic flood caused by intense rains that occurred in the period from August 5 to 8, 2017 in the south of Primorye, Russia. The Amba River (243 km2), where historical discharge peak was measured, was chosen as the test watershed of this study. Weather radar hourly precipitation fields with a spatial resolution of 1 km and a modified version of the kinematic-wave-based geomorphologic IUH model were used to estimate the hydrograph at the watershed outlet. The simulation was performed with an hourly time step; using the precipitation grids obtained from initial radar reflectivity data, and then using bias-correction based on precipitation measurement at the closest meteorological station. In the preliminary tests, the simulated discharge was found underestimated about two times in comparison with the observed discharges. After performing a precipitation bias-correction, the simulated and observed discharges showed good accordance. [ABSTRACT FROM AUTHOR]

This study validates three Aquarius sea surface salinity (SSS) Level 2 products against Argo salinity in the Bay of Bengal (BoB) for the period 26 August 2011 through 30 April 2015. The Aquarius SSS products used here include the standard product processed by the Aquarius Data Processing System (ADPS) and two data sets from the Jet Propulsion Laboratory (JPL): the Combined Active-Passive algorithm with and without rain correction (i.e. CAP and CAPRC). The bias and root-mean-square difference (RMSD) of the ADPS, CAP and CAPRC with respect to Argo salinity is −0.14 ± 0.60 psu, 0.03 ± 0.53 psu, and 0.07 ± 0.52 psu, respectively. A large negative bias (large RMSD) value is observed in the northern bay and along the Sumatra coast in the ADPS (all three Aquarius products) with a remarkable seasonal variation. We evaluate the errors induced by the geographic displacement and the difference in depth of the measurement and show that these errors are significant in regards to the validation of Aquarius in our study region. The strong negative bias is mainly caused by the near-surface salinity stratification. Frequent heavy precipitation events in the BoB induce a freshening bias of approximately −0.13 psu (mm h−1)−1 that lasted for less than 6 h, contributed 21% to the overall bias of the ADPS SSS and partly caused the high RMSD values observed along the Sumatra coast. The large SSS RMSD in the northern bay is largely induced by small-scale variations in SSS, and the overall RMSD is reduced by approximately 11% when the horizontal-SSS-gradient-induced RMSD is isolated. Therefore, the potential influences of the spatial representation errors associated with the spatial displacement between the satellite and in-situ measurements need to be considered when evaluating the satellite SSS values measured over marginal seas or plume regions that have rich-precipitation or strong small-scale SSS variations. [ABSTRACT FROM AUTHOR]

As a tipping bucket rain gauge, the HOBO Data Logging Rain Gauge RG3-M (RG3-M) has been widely used for the field precipitation observation owing to its superiority of independent power supply by a small portable battery. To quantify the measurement accuracy of the RG3-M gauge, a standard Manual Gauge (MG) and eight other models of tipping bucket rain gauges were installed at the Chuzhou hydrological experiment station of China. In this study, we first compared and investigated the accumulated mounts of 18 rainfall events of two RG3-M gauges benchmarked by the standard MG. Then, five typical rainfall events were chosen to further analyse the observed accuracy of the RG3-M gauge for different rainfall intensities at hourly temporal scale. Finally, the impacts of wind speed and rainfall intensity on the precipitation measurements of the RG3-M gauge were preliminarily explored. Results indicate that the RG3-M gauge measurement generally underestimates rainfall approximately −4% against the standard MG observation, but the maximum deviation even reaches −12.87%. In terms of the hourly rainfall process, the reliable measurement scope of the RG3-M gauge is ranging from 1.5 to 3 mm/h; however, it should be noted that the underestimation is rather significant at the higher rainfall rates (>6 mm/h). Last, it was found that rainfall intensity is a nonnegligible factor for influencing the measurement of the RG3-M gauge. But the windy effect seems to be insignificant in our experiments, which might be attributed to the similar exposure of the compared gauges. [ABSTRACT FROM AUTHOR]

Critical gaps in the amount, quality, consistency, availability, and spatial distribution of rainfall data limit extreme precipitation analysis, and the application of gridded precipitation data are challenging because of their considerable biases. This study corrected Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) in the Yarlung Tsangpo-Brahmaputra River Basin (YBRB) using two linear and two nonlinear methods, and assessed their influence on extreme precipitation indices. The results showed that the original APHRODITE data tended to underestimate precipitation during the summer monsoon season, especially in the topographically complex Himalayan belt. Bias correction using complementary rainfall observations to add spatial coverage in data-sparse regions greatly improved the performance of extreme precipitation analysis. Although all methods could correct mean precipitation, their ability to correct the wet-day frequency and coefficient of variation were substantially different, leading to considerable differences in extreme precipitation indices. Generally, higher-skill bias-corrected APHRODITE data are expected to perform better than those corrected by lower-skill approaches. This study would provide reference for using gridded precipitation data in extreme precipitation analysis and selecting bias-corrected method for rainfall products in data-sparse regions. [ABSTRACT FROM AUTHOR]

Four years (April 2014 to March 2018) of Global Precipitation Measurement (GPM) Precipitation Features data along with colocated the Modern Era Retrospective‐Analysis for Research and Applications‐2 model data are used to identify Freezing Rain Features (FRFs). A Precipitation Feature with presence of both melting layer (maximum temperature of the vertical column > 4 °C) and a layer of subfreezing air (2‐m temperature < 0 °C) adjacent to the surface is considered an FRF. During 4 years of observations, GPM and Modern Era Retrospective‐Analysis for Research and Applications‐2 identify approximately 3,096 FRFs globally (65°S–65°N). Most of them are observed over Northern Hemispheric land in the winter season. The majority of FRFs originates through the "melting process," whereas only 35 features are associated with "warm rain" process. The locations and seasonal and diurnal distribution patterns of the FRFs over the United States are well matched with the ground‐based observations. The ground‐based observations verify approximately 70% of the FRFs over the United States. Ku‐band radar properties show that FRFs are found shallower (2–5 km) and less intense (<27 dBZ) than precipitation features in general but deeper and more intense than Snow Features. Passive microwave properties show that FRFs Tbs and Polarization‐Corrected Temperature are warmer than Snow Features at all GPM Microwave Imager channels with the largest differences in 166 GHz. The enhancement in Tbs are more distinct with warm rain FRFs. FRF Tb tends to decrease as echo top height increases at all GPM Microwave Imager channels except for 183 GHz, where Tbs have lack of dependence on echo top height. Key Points: GPM Ku‐band radar and MERRA‐2 reanalysis data are used to identify Freezing Rain eventsRadar and Passive microwave properties of freezing rain events are summarizedPassive microwave properties show that FRFs Tbs and PCTs are warmer than SFs at all GMI channels [ABSTRACT FROM AUTHOR]

To define a dry day, the most common approach is to identify a fixed threshold below which precipitation is considered equivalent to zero. This fixed threshold is usually set to account for measurement errors and precipitation losses due to the atmospheric evaporation demand. Yet, this threshold could vary in time according to the seasonal cycle and in the context of long-term trends, such as the increase in temperature due to climate change. In this study, we compare extreme dry spells, defined either with a fixed threshold for a dry day (1 mm) or with a time-varying threshold estimated from reference evapotranspiration (ET 0), for a large database of 160 rain gauges covering large parts of the Mediterranean basin. Results indicated positive trends in ET 0 during summer months (June, July and August) in particular. However, these trends do not imply longer dry spells since the daily precipitation intensities remain higher than the increase in the evaporative demand. Results also indicated a seasonal behavior: in winter the distribution of extreme dry spells is similar when considering a fixed threshold (1 mm) or a time-varying threshold defined with ET 0. However, during summer, the extreme dry-spell durations estimated with a 1 mm threshold are strongly underestimated in comparison to extreme dry spells computed with ET 0. We stress the need to account for the atmospheric evaporative demand instead of using fixed thresholds for defining a dry day when analyzing dry spells, with respect to agricultural impacts in particular. [ABSTRACT FROM AUTHOR]

Wang, J.; Peng, T., and Shen, T., 2019. Temporal and spatial variation characteristics analysis of precipitation days and intensity over the Wujiang River Basin in flood season. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 209–219. Coconut Creek (Florida), ISSN 0749-0208. Based on the observation data of 41 surface meteorological stations in Wujiang River Basin from 1960 to 2016, the temporal and spatial variation laws of precipitation days and intensity in flood season (May to September) in Wujiang River Basin are analyzed. It is found that the distribution of precipitation in Wujiang River Basin is characterized by inhomogeneity of spatial change and tendency of temporal change. In terms of spatial distribution, affected by geographical position and topography, Wujiangdu is regarded as the boundary in terms of latitude. From the topographic point of view, because of the terrain in the vicinity of high mountains, precipitation frequency and value are larger, and precipitation also has a good correlation with the spatial distribution of temperature, humidity and wind speed; in the time variation, the frequency of rain days has an upward trend while the corresponding average precipitation is declining. The proportion of heavy rain days is decreasing while the corresponding average precipitation is increasing, and the relative humidity is decreasing. The surface air temperature is on the rise while the wind speed is on a fast downward trend. [ABSTRACT FROM AUTHOR]

Prior to the beginning of the World Meteorological Organization's (WMO) Solid Precipitation Inter-Comparison Experiment (SPICE, 2013–2015), two precipitation measurement intercomparison sites were established in Saskatchewan to help assess the systematic bias in the automated gauge measurement of solid precipitation and the impact of wind on the undercatch of snow. Caribou Creek, located in the southern boreal forest, and Bratt's Lake, located in the southern plains, are a contribution to the international SPICE project but also to examine national and regional issues in measuring solid precipitation, including regional assessment of wind bias in precipitation gauges and windshield configurations commonly used in Canadian monitoring networks. Overlapping with WMO-SPICE, the Changing Cold Regions Network (CCRN) Special Observation and Analysis Period (SOAP) occurred from 2014 to 2015, involving other enhanced observations and cold regions research projects in the same geographical domain as the Saskatchewan SPICE sites. Following SPICE, the two Saskatchewan sites continued to collect core meteorological data (temperature, humidity, wind speed, etc.) as well as precipitation observations via several automated gauge configurations, including the WMO automated reference and the Meteorological Service of Canada's (MSC) network gauges. In addition, manual snow surveys to collect snow cover depth, density, and water equivalent were completed over the duration of the winter periods at the northern Caribou Creek site. Starting in the fall of 2013, the core intercomparison precipitation and ancillary data continued to be collected through the winter of 2017. Automated observations were obtained at a temporal resolution of 1 min, subjected to a rigorous quality control process, and aggregated to a resolution of 30 min. The manual snow surveys at Caribou Creek were typically performed every second week during the SPICE field program with monthly surveys following the end of the SPICE intercomparison period. The Saskatchewan SPICE data are available at 10.18164/63773b5b-5529-4b1e-9150-10acb84d59f0 (Smith and Yang, 2018). The data collected at the Saskatchewan SPICE sites will continue to be useful for transfer function testing, numerical weather prediction and hydrological forecasting verification, ground truth for remote-sensing applications, as well as providing reference precipitation measurements for other concurrent research applications in the cold regions. [ABSTRACT FROM AUTHOR]

Western Nepal has experienced a severe drought in the past two decades, but observation records across Nepal are too short to place the recent drought in a longer context to understand the full range of natural variability in the climate system. In the present study we have collected tree core samples of Tsuga dumosa from two sites, Chhetti and Ranghadi, in the Api Nampa Conservation Area of the western Nepal Himalayas to understand drought variation for the past three centuries. We have developed a 357-year (AD 1657-2013) tree-ring chronology. The tree growth-climate response analysis revealed a stronger positive correlation with spring (March-May) standardized precipitation evapotranspiration index (SPEI01) (r = 0.523, p < 0.01) than precipitation (r = 0.459, p < 0.01), self-calibrating Palmer drought severity index (scPDSI) (r = 0.250, p < 0.01), or temperature (r = -0.486, p < 0.01). Stronger positive correlation with SPEI01 indicates moisture availability is the limiting factor for the growth of this species on these sites. Based on this growth-climate response we reconstructed spring SPEI from AD 1707 to 2013 for the region. The reconstruction showed several dry and wet episodes indicating no persistent climate trend within the past three centuries. The current drought is one of the four most severe in our 307-year record. [ABSTRACT FROM AUTHOR]

Heavy ice precipitation (HIP) flags in the products of dual‐frequency precipitation radar (DPR) on board the Global Precipitation Measurement (GPM) core satellite indicate the existence of large ice particles. In this study, we analyzed the distribution of a HIP band detected along the warm front of an oceanic extratropical cyclone. The HIP band was more than 125 km long and probably exceeded the 245‐km range of the Ku‐band precipitation radar. We found two zones with distinct microphysical processes in the HIP band. Ice particles near the echo top height showed little change in their median size with height, where frontogenetical updraft was maximum. This suggests that particles are generated and grown in this zone by the updrafts. In the zone below, the particle sizes increased as the height decreased, implying particles aggregate as they fall. Plain Language Summary: Bands of heavy snowfall are often reported in the presence of extratropical cyclones over land. However, observations of bands of heavy snowfall over the ocean are limited. In this study, we detected a band of heavy snowfall occurring over the ocean using satellite‐borne precipitation radar for the first time. This signal had a narrow width of 10–20 km and a length that was likely greater than the 245‐km swath of the Ku‐band radar. We examined the change in precipitation echoes with height for the snowfall to understand how ice particles grow and subsequently result in heavy snowfall. The analysis suggested that in the upper layer of the heavy snowfall band, ice particles are produced and grown by an upward flow, and that in the middle layer, they collide with other ice crystals, resulting in larger crystals, which subsequently fall. Further statistical analysis of similar signals could reveal the structure and driving mechanisms for heavy snowfall over the land and ocean in future studies. Key Points: We detected a heavy ice precipitation (HIP) band along the warm front of an oceanic cyclone observed by GPM/DPRIce particles are grown by updraft near the echo top height of the HIP band and then aggregate in the zone below [ABSTRACT FROM AUTHOR]

The initial-state sensitivity and optimal perturbation growth for 24- and 36-h forecasts of low-level kinetic energy and precipitation over California during a series of atmospheric river (AR) events that took place in early 2017 are explored using adjoint-based tools from the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). This time period was part of the record-breaking winter of 2016–17 in which several high-impact ARs made landfall in California. The adjoint sensitivity indicates that both low-level winds and precipitation are most sensitive to mid- to lower-tropospheric perturbations in the initial state in and near the ARs. A case study indicates that the optimal moist perturbations occur most typically along the subsaturated edges of the ARs, in a warm conveyor belt region. The sensitivity to moisture is largest, followed by temperature and winds. A 1 g kg−1 perturbation to moisture may elicit twice as large a response in kinetic energy and precipitation as a 1 m s−1 perturbation to the zonal or meridional wind. In an average sense, the sensitivity and related optimal perturbations are very similar for the kinetic energy and precipitation response functions. However, on a case-by-case basis, differences in the sensitivity magnitude and optimal perturbation structures result in substantially different forecast perturbations, suggesting that optimal adaptive observing strategies should be metric dependent. While the nonlinear evolved perturbations are usually smaller (by about 20%, on average) than the expected linear perturbations, the optimal perturbations are still capable of producing rapid nonlinear perturbation growth. The positive correlation between sensitivity magnitude and wind speed forecast error or precipitation forecast differences supports the relevance of adjoint-based calculations for predictability studies. [ABSTRACT FROM AUTHOR]

To date, influence of aerosols on the atmospheric stability and hence on the convection/precipitation is not well understood. From the detailed analysis carried out using high‐accuracy radiosonde and Aethalometer measurements, a significant decrease in the convective available potential energy has been noticed in association with the increase in surface concentration of black carbon (BC) for a restricted range of precipitable water vapor and under similar environmental background (wind speed, direction, and cloud cover) over Gadanki (13.5°N, 79.2°E), a tropical rural site in southern peninsular India. A number of case studies along with statistical analysis indicate a notable perturbation in the temperature profile associated with high‐surface BC concentration conditions compared to the regional mean concentration of the same. A discernible fall in the temperature lapse rate within and above the local boundary layer in association with the higher BC concentration has been observed, which in turn reduces the available potential energy for convection decreasing the positive buoyancy required for a rising moist air parcel. This observation has been noticed over a nonurban location, but the phenomenon is not location dependent and is more applicable to the urban regions where BC concentration is likely to be much higher. Plain Language Summary: Our daily needs of energy lead us to massive usage of fuel burning. These burnings release huge amount of tiny particles into the atmosphere, which significantly influence our environment. Black carbon (BC) is such a man‐made health hazardous pollutant aerosol particle which warms up our environment by absorbing solar radiation. Generally, the temperature of our surroundings decreases with height in a certain rate. Due to this nature of temperature in our atmosphere, the warm moist air from the ground can move upward and is cooled down. The upward movement of moist air eventually causes rainfall mostly during summer in tropical areas. But our present observation shows that higher amount of BC in our environment can disturb the normal upward movement of moist air by heating up the atmosphere. Thus, the increase in the amount of this man‐made pollutant particle affects not only human health but also the normality of our atmosphere disturbing its balance. Our study provides some important evidences on how BC particles can disturb the balance of our atmosphere. This observation has been done over a rural location, but the phenomenon should be more prominent over urban environment where BC is likely to be more in amount. Key Points: Influence of black carbon on the atmospheric instability is investigated under similar background conditionsIt is found that increase in surface black carbon affects the lower tropospheric instability over a regionThere is significant decrease in temperature lapse rate and convective available potential energy during increase in black carbon concentration [ABSTRACT FROM AUTHOR]

Microphysical (MP) schemes contain parameters whose values can impact the amount and location of forecasted precipitation, and sensitivity is typically explored by perturbing one parameter at a time while holding the rest constant. Although much can be learned from these "one-at-a-time" studies, the results are limited as these methods do not allow for nonlinear interactions of multiple perturbed parameters. This study applies the Morris one-at-a-time (MOAT) method, a robust statistical tool allowing for simultaneous perturbation of numerous parameters, to explore orographic precipitation sensitivity to changes in microphysical and environmental parameters within an environment characteristic of an atmospheric river. Results show parameters associated with snow fall speed coefficient As and density ρs, ice-cloud water collection efficiency (ECI), rain accretion (WRA), relative humidity, zonal wind speed, and surface potential temperature cause the largest influence on simulated precipitation. MP and environmental parameter perturbations can cause precipitation changes of similar magnitude, but results vary by location on the mountain. Different environments are also tested, with As being the most influential MP parameter regardless of environment. Fewer MP parameters influence precipitation in a faster-wind-speed environment, possibly due to the stronger dynamical forcing upwind and different wave dynamics downwind, compared to a slower-wind-speed environment. Finally, perturbing MP parameters within a single scheme can result in precipitation variations of similar magnitude compared to using entirely different microphysics schemes. MOAT results presented in this study have implications for Bayesian parameter estimation methods and stochastic parameterization within ensemble forecasting. [ABSTRACT FROM AUTHOR]

To date, most studies of citizen science engagement focus on quantifiable measures related to the contribution of data or other output measures. Few studies have attempted to qualitatively characterize citizen science engagement across multiple projects and from the perspective of the participants. Building on pertinent literature and sociocultural learning theories, this study operationalizes engagement in citizen science through an analysis of interviews of 72 participants from six different environmentally based projects. We document engagement in citizen science through an examination of cognitive, affective, social, behavioral, and motivational dimensions. We assert that engagement in citizen science is enhanced by acknowledging these multiple dimensions and creating opportunities for volunteers to find personal relevance in their work with scientists. A Dimensions of Engagement framework is presented that can facilitate the innovation of new questions and methodologies for studying engagement in citizen science and other forms of informal science education. [ABSTRACT FROM AUTHOR]

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins. [ABSTRACT FROM AUTHOR]

The measurement of precipitation is essential for most environmental studies such as drought monitoring, watershed operations, water hazard management, etc. Development of satellite products has improved their applicability in environmental modelling and could proffer an alternative to gauge-based precipitation data, particularly in areas where there is no sufficient number of gauges or poor gauge distribution but they should be evaluated in different areas using ground-based data as references. In the present study, daily Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM- IMERG- Final (Version 5)) and Global Satellite Mapping of Precipitation-Moving Vector with Kalman filter (GSMaP-MVK (Version 7)) precipitation products were evaluated in comparison with gauges observations in Ardabil province, north-west of Iran, from 1 January 2016 to 21 October 2017. Several statistical indices including linear correlation coefficient, Bias (B), Multiplicative Bias (Bm), Relative Bias (Br), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) were used for evaluation. The results showed that the correlation between GSMaP estimates and gauge observations is higher than that of IMERG (0.42 and 0.33, respectively). On the other hand, GSMaP tends to overestimate precipitation substantially, while IMERG is involved in both under and overestimation slightly. Although these products could not show very high accuracy in precipitation estimation, the estimated precipitation values by IMERG were relatively closer to gauge records and can be used as a replacement for gauge observation in the study area where there is lack of weather stations. [ABSTRACT FROM AUTHOR]

Use of the standard deviation σm of the drop mass distribution as one of the three parameters of raindrop size distribution (DSD) functions was introduced for application to disdrometer data supporting the Global Precipitation Measurement dual-frequency radar system. The other two parameters are a normalized drop number concentration Nw and the mass-weighted mean diameter Dm. This paper presents an evaluation of that formulation of the DSD functions, in two parts. First is a mathematical analysis showing that the procedure for estimating σm, along with the other DSD parameters, from disdrometer data is in essence another moment method. As such, it is subject to the biases and errors inherent in all moment methods. When the form of the DSD function is specified, it is inferior (like all moment methods) to the maximum likelihood technique for fitting parameters to sampled data. The second part is a series of sampling simulations illustrating the biases and errors involved in applying the formulation to the specific case of gamma DSDs. It leads to underestimates of σm and consequently to overestimates of the gamma shape parameter—with large root-mean-square errors. Comparison with maximum likelihood estimates shows the degree of improvement that could be obtained in the estimates of the shape parameter. The propensity to underestimate σm will be pervasive, and users of this DSD formulation should be cognizant of the biases and errors that can occur. [ABSTRACT FROM AUTHOR]

Recently, extreme climate variation has been studied in different parts of the world, and the present study aims to study the impacts of climate extremes on vegetation. In this study, we analyzed the spatiotemporal variations of temperature and precipitation extremes during 1960–2015 in the Yangtze River Basin (YRB) using the Mann-Kendall (MK) test with Sen's slope estimator and kriging interpolation method based on daily precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin). We also analyzed the vegetation dynamics in the YRB during 1982–2015 using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) datasets and investigated the relationship between temperature and precipitation extremes and NDVI using Pearson correlation coefficients. The results showed a pronounced increase in the annual mean maximum temperature (Tnav) and mean minimum temperature (Txav) at the rate of 0.23 °C/10 years and 0.15 °C/10 years, respectively, during 1960–2015. In addition, the occurrence of warm days and warm nights shows increasing trends at the rate of 1.36 days/10 years and 1.70 days/10 years, respectively, while cold days and cold nights decreased at the rate of 1.09 days/10 years and 2.69 days/10 years, respectively, during 1960–2015. The precipitation extremes, such as very wet days (R95, the 95th percentile of daily precipitation events), very wet day precipitation (R95p, the number of days with rainfall above R95), rainstorm (R50, the number of days with rainfall above 50 mm), and maximum 1-day precipitation (RX1day), all show pronounced increasing trends during 1960–2015. In general, annual mean NDVI over the whole YRB increased at the rate of 0.01/10 years during 1982–2015, with an increasing transition around 1994. Spatially, annual mean NDVI increased in the northern, eastern, and parts of southwestern YRB, while it decreased in the YRD and parts of southern YRB during 1982–2015. The correlation coefficients showed that annual mean NDVI was closely correlated with temperature extremes during 1982–2015 and 1995–2015, but no significant correlation with precipitation extremes was observed. However, the decrease in NDVI was correlated with increasing R95p and R95 during 1982–1994. [ABSTRACT FROM AUTHOR]

Based on the observation data of 41 surface meteorological stations in Wujiang River Basin from 1960 to 2016, the temporal and spatial variation laws of precipitation days and intensity in flood season (May to September) in Wujiang River Basin are analyzed. It is found that the distribution of precipitation in Wujiang River Basin is characterized by inhomogeneity of spatial change and tendency of temporal change. In terms of spatial distribution, affected by geographical position and topography, Wujiangdu is regarded as the boundary in terms of latitude. From the topographic point of view, because of the terrain in the vicinity of high mountains, precipitation frequency and value are larger, and precipitation also has a good correlation with the spatial distribution of temperature, humidity and wind speed; in the time variation, the frequency of rain days has an upward trend while the corresponding average precipitation is declining. The proportion of heavy rain days is decreasing while the corresponding average precipitation is increasing, and the relative humidity is decreasing. The surface air temperature is on the rise while the wind speed is on a fast downward trend. [ABSTRACT FROM AUTHOR]

Abstract This study evaluates the performance of latest version 5B (V5B) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Final Run products during a 60-year return extreme precipitation storm on 7 May 2017 over southern China with gauge observations as the reference dataset. Version 4 (V4) Global Satellite Mapping of Precipitation (GSMaP) products and quantitative precipitation estimates derived from a local ground-based S-band dual polarization weather radar (radar, hereafter) were used for parallel comparisons. The satellite-only products (IMERGUncal and GSMaP_MVK) and gauge-corrected products (IMERGCal, GSMaP_Gauge) were selected for this study. The results showed that: 1) GSMaP_MVK, IMERGUncal, GSMaP_Gauge and IMERGCal generally capture the spatio-temporal patterns of storm-accumulated rainfall with correlation coefficient (CC) values about 0.76, 0.70, 0.68 and 0.72, respectively, while radar was well correlated with gauge measurement (CC about 0.94); 2) The GSMaP_Gauge (−19.38%), IMERGCal (−40.23%), GSMaP_MVK (−57.12%), IMERGUncal (−58.77%) satellite-based precipitation products all underestimated the storm-accumulated precipitation, while ground radar overestimated by 27.48%; 3) Both IMERGCal and IMERGUncal outperformed their GSMaP counterparts in capturing the time-series with much higher CC (0.50 vs. -0.21, 0.51 vs. 0.17); 4) Among the satellite-based QPE products, when the rainfall rates are <5 mm/h, IMERGCal performs best with highest CSI. The V4 GSMaP standard products and latest V5B IMERG final run products still had resolution and accuracy limitations in estimating extreme precipitation for this type of warm sector rainfall storm. These findings will help developers of rainfall retrieval products to understand the errors and also help improve modeling of hydrological extremes. Highlights • V5B IMERG performed better than V4 GSMaP during extreme rainfall storm • All satellite-based QPE products generally captured the spatial patterns • IMERG outperformed GSMaP for hourly series of rainfall • Radar can serve as a good tool for extreme rainfall monitoring. [ABSTRACT FROM AUTHOR]

Abstract Accurate and continuous precipitation amount and spatiotemporal distribution data are essential for water resource management, flood forecasting, and simulation of hydrological processes. Satellite-based precipitation products (SPPs) with high spatio-temporal resolution have been shown to represent a potential alternative data source for ungauged regions. The objective of this study was to evaluate the performance and the hydrological utility of a SPP (TMPA 3B42V7) driving two different hydrological models (Xinanjiang and Tank models) for runoff monitoring in the Yangtze River Basin in China. The results indicate that the TMPA precipitation data can be applied for runoff simulation compared to the ground-gauged data, and the adjustment of TMPA data can be helpful to improve the accuracy of rainfall data at the point and region scale. Two adjustment equations were proposed in this study (the geographical adjustment model and the additive error adjustment model), and the Bias had significantly improved, which solve the problem of data undervaluation. In addition, the TMPA precipitation data show an acceptable accuracy for driving the Xinanjiang and Tank models for the purpose of runoff monitoring. By utilizing the measured runoff data from the four main stations of the Yangtze River Basin, an excellent performance was shown in Xinanjiang model with the determined coefficient of 0.945–0.966 and 0.900–0.922 for the calibration and validation periods, respectively. This was similar for the Tank model with values of 0.943–0.963 and 0.917–0.920 for the calibration and validation periods, respectively. Based on these results, TMPA data would be helpful not only for understanding the characteristics of precipitation at high spatial and temporal resolution, but also for estimating near-real time runoff patterns. Highlights • The accuracy and performance of the SPP (TMPA 3B42V7) was evaluated by considering two adjustment methods. • The XAJ and Tank models were recalibrated with parameter sets using the SPPs, and then validated over the main section for runoff prediction. • The best framework to utilize SPP in runoff monitoring was explored in the Yangtze River Basin (YRB). [ABSTRACT FROM AUTHOR]

The French Guiana (80 000 km2) is highly vulnerable to flooding during the rainy season but the hydrological prevision is limited because the region cannot be cover by a dense network of rain gauges. Meteorological satellites could be an alternative for the measurement of precipitation. The objective of this paper was to evaluate and improve the accuracy of daily satellite rainfall estimates (SRE) throughout the French Guiana between April 2015 and March 2016. Validation data were composed by 70 rain gauges managed by France and Suriname. Three satellite-based rainfall estimates have been tested: TRMM-TMPA 3B42 (Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis) V7, IMERG (Integrated Multi-satellitE Retrievals) for GPM (Global Precipitation Measurement) and STAR Satellite rainfall estimates Hydro-Estimator (HE). Better SRE were obtained by GPM with a clearly higher probability of detection of rainy days (>70%). During the rainy season, biases remained important and SRE appeared inaccurate for the monitoring and forecasting of floods. Biases correction methods were applied, and the additive correction methods by interpolation of biases (ADD_IDW) obtained the better performance (absolute biases <8 mm day−1; RMSE <12 mm day−1) for each satellite products. This simple method proved to be very effective to reduce biases close to 0 throughout the year. After ADD_IDW correction, performance levels of TRMM, GPM, and HE products were relatively close and these three satellite products could be implemented into cascade chains in operational framework ensuring the provision of corrected SRE in real time and thus guarantee a reinforced hydrological monitoring in French Guiana. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the link of the lowest sea-ice extent on record with the lowest precipitation event over parts of western Europe, and the anthropogenic contribution to the climate change. Data and methods used in the study are described, including sea ice concentration, European precipitation, and sea level pressure. Three sets of retrospective forecasts to attribute the reduced Arctic sea ice conditions with the lowest European precipitation event are noted.

A field observation was carried out along the coast of the Japan Sea in the 2016-2017 and 2017-2018 winter seasons, using the Ground-based Particle Image and Mass Measurement System (G-PIMMS) to evaluate the Global Precipitation Measurement Mission (GPM) dual-frequency precipitation radar (DPR) precipitation type classification algorithm. The G-PIMMS was installed at Kanazawa University and Ishikawa Prefectural University, which are around 10 km apart from each other. The G-PIMMS observations showed that the major precipitation particle type (graupel or snowflake) was different in the precipitation types classified by the GPM DPR algorithm. [ABSTRACT FROM AUTHOR]

On Thursday May 14, 2015, an active front passed over the greater Wellington region of New Zealand with embedded bands of intense convection and very high rain rates. The front had a northwest to southeast orientation and the winds were predominantly from the northwest so the front moved along its length meaning the intense rainfall associated with the embedded organised bands was able to remain in place for up to half an hour or so. This led to severe localised flooding with return periods of just over 100 years in many areas from Waikanae southwards. Local models accurately forecast aspects of this event, for example rain, totals of up to 40 mm in an hour, but generally failed to get both the locations and timings of the most intense rain correct. In general, the forecast rain totals improved with model resolution but there was considerable variation depending on initial analysis time and the latest forecasts were not always the best. In this paper we investigate the key features that contributed to the intense rain and whether they can be simulated with current operational weather models. We also note the existence of a low level moist absolutely unstable layer in the lowest 2 km arising from advection of very high specific humidity air from the subtropics through the width of the front. [ABSTRACT FROM AUTHOR]

Accurate prediction of the location and timing of landslides is essential for mitigating physical, social and economic risk. Rainfall (alongside earthquakes) is one of the most significant triggers for landslides, thus more detailed spatial and temporal rainfall data could help improve current landslide prediction models. This study contrasts the spatial characteristics of rain-radar observations to conventional rain gauge approaches within the context of Rainfall Induced Landslide Hazard Models (RILHM). An empirical RILHM based on underlying terrain slope and rainfall accumulation has been implemented and the response when driven with high resolution radar rainfall accumulations or a hypothetical sparse gauge network has been investigated. The skill of the conceptual model for identifying the landslide risks showed a False Alarm Ratio of 0.67 (the fraction of predicted events that did not occur) and a Probability of Detection (the fraction of observed events that were correctly forecast) of 0.42 when compared with the rain-radar driven model. This implies that the significant spatial sampling uncertainties associated with sparse rain gauge measurements are likely to be an important source of forecast error in any operational RILHM. [ABSTRACT FROM AUTHOR]

Sunshine duration in New Zealand has historically been measured using a Campbell-Stokes sunshine recorder. The contemporary upgrade of climate stations has resulted in the introduction of Kipp & Zonen CSD sunshine recorders. In this study, different sunshine durations have been measured by each instrument operating in parallel at the same climate station location. We aimed to compare the difference between the two measurement methods, to make an informed judgement about 1) which instrument is more suitable for measuring sunshine duration, and 2) whether homogenisation of New Zealand's historic Campbell- Stokes data with subsequent Kipp & Zonen data are achievable. Campbell-Stokes and Kipp & Zonen sunshine duration data from four locations were assessed in this study, and these sunshine durations were validated against radiation-derived sunshine duration data. On average, Kipp & Zonen sunshine durations were higher than Campbell-Stokes sunshine durations at all four sites, with the difference ranging from 2% at Invercargill to 17% at Hokitika. When compared to radiation-derived sunshine duration data, Kipp & Zonen sunshine data were more accurate (mean absolute errors of 0.07-0.09 hours) compared to Campbell- Stokes data (mean absolute errors of 0.22-0.32 hours). We propose that the Kipp & Zonen instrument is more suitable for the measurement of sunshine duration compared to the Campbell-Stokes instrument. Without an understanding of the intra- and inter-site variability of sunshine durations measured by each instrument, the homogenisation of historic Campbell-Stokes data with subsequent Kipp & Zonen data would prove challenging and ultimately unsuitable. [ABSTRACT FROM AUTHOR]

This paper compares multiple gridded data sets of daily UK precipitation to evaluate structural uncertainty in our reconstructions of historical rainfall. The data sets compared reflect two different sampling strategies and three different grid interpolation methods. In order to separate the influence of sampling and interpolation uncertainties, one of the data sets (produced by the Met Office) has been recreated using the sampling strategy of stations used in the European (E‐OBS) data set. The results confirm and build upon previous studies showing that relying on a relatively sparse but homogeneous network of stations limits the ability of the resulting data set to reliably estimate extreme rainfall at the daily timescale. It is shown that gridding methods that additionally make use of reference climatological data can avoid systematic bias in both the average and extreme events even when using a relatively sparse network of observations. This is an encouraging result in terms of our potential to reliably extend such data sets further back in time where the availability of digitized data is substantially lower than the more modern era. This paper compares multiple gridded rainfall data sets for the UK to analyse structural uncertainty in historical reconstructions of rainfall. In particular, as well as highlighting the shortcomings of using a sparse network of long‐running rainfall stations (as per E‐OBS), this paper shows that gridding methods that make use of reference climatological data help avoid systematic bias. This is a promising and valuable result for purposes of extending rainfall series back in time. [ABSTRACT FROM AUTHOR]

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Precipitation is one of the most important abiotic variables related to plant growth. Using standardised measurements improves the comparability and quality of precipitation data as well as all other data within the Integrated Carbon Observation System network. Despite the spatial and temporal variation of some types of precipitation, a single point measurement satisfies the requirement as an ancillary variable for eddy covariance measurements. Here the term precipitation includes: rain, snowfall (liquid water equivalent) and snow depth, with the latter two being of interest only where occurring. Weighing gauges defined as Integrated Carbon Observation System standard with the capacity of continuously measuring liquid and solid precipitation are installed free-standing, away from obstacles obstructing rain or snowfall. In order to minimise wind-induced errors, gauges are shielded either naturally or artificially to reduce the adverse effect of wind speed on the measurements. Following standardised methods strengthens the compatibility and comparability of data with other standardised environmental observation networks while opening the possibility for synthesis studies of different precipitation measurement methodologies and types including a wide range of ecosystems and geolocations across Europe. [ABSTRACT FROM AUTHOR]

This study aims to assess the reliability of satellite-precipitation products from the Global Precipitation Measurements ( GPM ) mission in regions with complex landscape morphology. Our analysis is carried out in the European mid-latitude area, namely on the two major islands of Mediterranean Sea, i.e. Sardinia and Sicily (Italy). Both islands experience precipitation originating from the interaction of steep orography on the coasts with winds carrying humid air masses from the Mediterranean Sea. The GPM post real-time IMERG (Integrated Multi-satellitE Retrievals from Global Precipitation Measurement) “Final” run product at 0.1° spatial resolution and half-hour temporal resolution have been selected for the two-year 2015–2016 period. Evaluation and comparison of the selected product, with reference to raingauge network data, are performed at hourly and daily time scales using statistical and graphical tools. The influences of morphology and land-sea coastal area transition on the reliability of the GPM product have been analysed. Confirming previous studies, results showed that GPM satellite data slightly overestimate rainfall over the study areas, but they are well correlated with the interpolated raingauge data. Metrics based on occurrences above a given threshold and on total volume above the same threshold were applied and revealed better performances for the latter ones. Applying the same metrics we show how GPM performances improve as the temporal aggregation increases. Several drawbacks were detected in the coastal areas, which were characterized by worse performances than internal areas. Statistics are generally very similar for the two considered case studies (i.e., Sardinia and Sicily) except for correlation between topography and accuracy of GPM products, which was slightly higher for Sardinia. [ABSTRACT FROM AUTHOR]

A multicriteria score-based method was developed to assess the performances of 18 general circulation models (GCMs) in the study region from 1970 to 2005. The results indicate the following. (1) GCMs simulate temperature better than rainfall. The temporal and spatial distributions of simulated temperature performed well compared with those from the observations. In comparison to temperature, the spatial distribution of simulated precipitation performed poorly. Most of the GCMs underestimated temperature and overestimated precipitation. (2) The Grubbs test was used to detect anomalous moving changes in the rank score (RS) results; the inm-cm4 and ipsl-cm5b-lr models were rejected when simulating temperature, while the bnu-esm and canesm2 models performed poorly when simulating precipitation. (3) Adding or removing any criterion does not significantly influence the RS result, which indicates that the multicriteria score-based method is robust. The advantages of using multicriteria score-based method to assess GCMs performance were demonstrated, and this method also provides a more comprehensive assessment when compared with the single-criterion method. The multicriteria method could replace other criteria as the research requirements and could be easily extended to different study regions; the results could be used for better informed regional climate change impact analyses. [ABSTRACT FROM AUTHOR]