Precipitation Measurement Research Articles

Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) provides new-generation satellite precipitation datasets with high spatio-temporal resolution and accuracy, which is widely applied in hydrology and meteorology. However, most examinations of the IMERG were conducted on daily, monthly, and annual scales, and inadequate research focused on the sub-daily scale. Thus, this study set up four sub-daily scales (1 h, 3 h, 12 h, and 24 h at 0.1° spatial resolution) and four spatial scales (0.1°, 0.25°, 0.5°, and 1° at 1 h temporal resolution) to finely evaluate the performance of IMERG products in the summer seasons from 2014 to 2019 over mainland China. The precipitation amount (PA), frequency (PF), and intensity (PI) were adopted to assess the performance of the IMERG referenced by the ground-based precipitation product of the China Meteorological Administration (CMA). The results show that the IMERG can capture the spatial patterns of precipitation characters over mainland China, but the PA and PI are overestimated and the PF is underestimated, and the evaluation results are highly sensitive to the different temporal and spatial resolutions. Compared with fine spatio-temporal scales, the performance of the IMERG is significantly improved when scaled up to coarser scales. Moreover, the IMERG shows a better performance of PA and PI in larger regions and during longer periods. This study provided a reference for the application of IMERG products in different spatial and temporal scales.

A multiple-step scheme for the improvement of satellite precipitation products over the Tibetan Plateau from multisource information

Precipitation data with high accuracy and spatial resolution characteristics play significant roles in the regional hydrological and eco-environmental system applications. Thus, satellite-based precipitation products (SPPs) with high spatial resolution and high accuracy must be developed. This study proposed a multiple-step scheme to improve the global precipitation measurement (GPM) at daily and monthly timescales over the Tibetan Plateau (TP) from 2014 to 2017. First, combined with the geographically weighted regression (GWR) method using geographic and topographic factors, the gamma-distribution mapping and local intensity scaling (GDM-LOCI) method is applied to effectively merge the observed data attributes and the spatial representation of SPPs at the daily scale by correcting precipitation volumes and frequencies. Second, the areal merged precipitation, normalized difference vegetation index (NDVI), and reanalyzed atmospheric data are used to improve the spatial resolution of monthly GPM with a random forest (RF) model that uses the 17 land cover types to establish the local downscaling model windows. The results show that daily merged precipitation can better reflect the spatial and temporal variability of precipitation than can satellite estimates, and the correlation coefficient (R), and critical success index (CSI) increased by 0.12 and 0.26, respectively. In the merged downscaling model, the merged precipitation factor can weaken the negative effect of the other auxiliary predictors due to its spatial autocorrelation in precipitation estimation. Most importantly, by using the land cover types to establish local model windows for the downscaling model, not only the spatial resolution of the GPM product is downscaled to 1 km, but also the spatial structure of the downscaled products is enhanced, with less deviation and a higher spatial correlation. The R, the root mean square error (RMSE) and the relative bias (BIAS) were 0.89, 50.19 mm and 0.57, respectively. This study presents a promising scheme for generating high-quality precipitation data for regional hydrometeorological research in data-scarce regions.

Comprehensive Analysis of Typhoon Nangka Based on the Satellite Data from the GPM, CloudSat and Himawari-8

A typhoon or hurricane is one of the most destructive high-impact weather events. In this study, the genesis and development processes of Typhoon Nangka (2015), which occurred over the Western Pacific in 2015, were investigated based on the comprehensive observation data from three satellites, i.e., the Himawari-8 satellite, the CloudSat satellite and the Global Precipitation Measurement mission satellite (GPM), focusing on the characteristics of typhoon structure, precipitation and cloud. The results (Results) show that during the developing stage of Typhoon Nangka, the cloud system was relatively complex and changed significantly, with large raindrops dominating the precipitation around the eyewall in the first quadrant, and the convection in the eyewall and outer rainband burst upward to 17 km. In addition, three features were obvious: stratiform precipitation was dominant in the inner rainband, both the precipitation type (stratiform or convective) and intensity were distributed unevenly in the outer rainband, and large water content was located in the warm layer of clouds. Moreover, the collision growth and breakup of water droplets tended to be stable. The precipitation in the typhoon eyewall, inner rainband and outer rainband was significantly different; stratiform precipitation mainly occurred in the inner rainband, while convective precipitation mainly appeared in the eyewall and outer rainband. The cloud system was distributed asymmetrically, and the upper-layer and lower-layer clouds were closely related, dominated by single-layer clouds. There were deep convective clouds in the eyewall, and cirrus clouds with the broadest range across the eyewall. The coverage range of cirrus clouds was close to the radius of the typhoon. There were stratocumulus, altostratus and cumulus in the low levels.

Space–time variation of large hail‐producing mesoscale convective systems over a complex terrain of the Indian subcontinent as revealed by the integrated Tropical Rainfall Measuring Mission and Global Precipitation Measurement observations

AbstractSpace–time variation of large hail‐producing mesoscale convective systems (MCSs) is investigated over the eastern and northeastern parts of the Indian subcontinent during the premonsoon (March–April–May) season by using long‐term (1998–2020) integrated observations of Precipitation Radar and Microwave Imager onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites. A threshold of 37.0‐GHz polarization‐corrected temperature (≤176 K) is utilized as a hail proxy. Qualitatively, the space–time variation of satellite‐detected hailstorms is reasonably in good agreement with the available hail reports at the ground. The large hail‐producing MCSs are most frequently observed over the plains, whereas such MCSs are not found over mountains. The MCSs in April have the highest probability to contain hail though the hail‐producing MCSs are more common in May. The average morphological and microphysical properties are distinctly different for the MCSs with and without hail. Compared to the active convective cores of MCSs without hail, the hail‐producing convective cores of MCSs (a) are extended higher vertically and wider horizontally, (b) are associated with much larger area (~1,000 vs. 100 km2) occupied by radar reflectivity larger than 40 dBZ in the mixed‐phase region, and (c) are associated with larger values of cloud ice water content (CIWC; 395 vs. 153 mg·m−3) in the mixed‐phase region. The results from the high‐resolution ERA5 reanalysis data show that the hail‐producing MCSs are more sensitive to synoptic forcing than the MCSs without hail. Very strong mean sea‐level pressure anomalies over the whole northern part of India along the Himalayan foothills to the Bay of Bengal occur for the MCSs with hail days. The findings of this study will help the forecasting of these hailstorms and mitigation of their damage within this less explored region.

Utilizing Satellite Data to Establish Rainfall Intensity-Duration-Frequency Curves for Major Cities in Iraq

This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corrected (GSMaP GC) satellite precipitation datasets. Many probability distribution functions were used to fit the maximum yearly rainfall data. The Sherman equation was used to create intensity-duration-frequency (IDF) curves for rainfall intensities with 2-, 5-, 10-, 25-, 50-, and 100-year return periods, with the estimated coefficients of the best-fit distribution serving as the fitting parameters. The discrepancy between the IDF curves produced from the satellites and the observed data was used to bias correct the satellite IDF curves. The Generalized Extreme Value Distribution model best describes the hourly rainfall distribution of satellite data. GSMaP GC was the best option for creating IDF curves with higher correlations with observed data at Baghdad, Basra, and Mosul. The study indicates the necessity of gauge correction of satellite rainfall data to reduce under- and over-estimating observed rainfall. GSMaP GC can reasonably estimate rainfall in a predominantly arid climate region like Iraq. The generated IDF curves may be an important step toward achieving sustainable urban stormwater management in the country.

Climatic and Vegetation Response Patterns over South Africa during the 2010/2011 and 2015/2016 Strong ENSO Phases

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation.

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC’s topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency.

Assessment of GPM IMERG and GSMaP daily precipitation products and their utility in droughts and floods monitoring across Xijiang River Basin

The recent increasing concerns about floods and droughts have brought up a pressing need for high-precision and high-resolution precipitation records. As representative satellite precipitation products (SPPs) in Global Precipitation Measurement (GPM) era, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product and the Global Satellite Mapping of Precipitation (GSMaP) are widely acknowledged as reliable alternative sources. A quantitative evaluation of these new products is of great importance before their extended applications. This study comprehensively assesses the statistical performance and the application potential of IMERG (IMERG-Early, IMERG-Late and IMERG-Final) and GSMaP (GSMaP-NRT, GSMaP-Gauge-NRT, GSMaP-Gauge) products over Xijiang River Basin during 2009–2018 at a daily timescale. Both IMERG and GSMaP products have their own advantages. The results indicate that (1) at the grid scale, the IMERG-Final exhibits the highest correlation with gauge-based precipitation data while GSMaP-Gauge yields the lowest relative mean error. IMERG-Early and IMERG-Late have superiorities in almost all evaluation metrics relative to GSMaP-NRT and GSMaP-Gauge-NRT. (2) The temporal performance in specific months visually demonstrates that GSMaP-Gauge has a lower precipitation detection capacity in wet seasons due to a higher FAR, in opposite to other SPPs. (3) The precipitation detection ability of SPPs varies largely with different precipitation intensities. GSMaP-Gauge can identify nearly 90% rainfall events at the lowest threshold of 1 mm/day, but it tends to overestimate the non- and slight precipitation events up to small or moderate classes and highly underestimate precipitation amount of strong events. At extreme precipitation intensity (≥50 mm/day), >50% of extreme precipitation events were missed by SPPs resulting in unsatisfactory PODs below 0.5. (4) For meteorological drought monitoring, IMERG-Final exhibits highest CCs in all inspected timescales, succeed by GSMaP-Gauge. IMERG-Early and IMERG-Late still perform better than near-real-time GSMaPs in drought research. (5) In the hydrological simulation with the Soil and Water Assessment Tool (SWAT), GSMaP-Gauge-simulation shows the best accuracy regardless of wet or dry seasons with highest NSEs of 0.64 and 0.73, respectively. All near-real-time SPPs present an unacceptable hydrological utility and an underestimation of streamflow in dry seasons. The systematic evaluation of extreme streamflow indices highlights that of the four timelier products, IMERG-Early is most reliable in capturing extreme streamflow. GSMaP-NRT tends to exaggerate the flood magnitude and could be used as reference in estimating severe and continuous floods.

Urban landscape and climate affect residents’ sentiments based on big data

Residents’ sentiments are a quantitative indicator of human feelings, which is useful for optimizing urban residential environments. Little is known about the spatiotemporal variations and potential drivers of sentiment based on big data. A total of 221,104 Weibo social media data were used to quantify daily sentiment in the Beijing metropolitan area during the COVID-19 pandemic from January 1, 2021 to March 8, 2022. Deep-learning natural language processing was used to extract this dataset to investigate the spatiotemporal sentiment patterns. The density of roads and buildings, normalized difference vegetation index (NDVI), population, sky visibility factor, daily land surface temperature (LST), daily precipitation, and daily air pollution concentrations (CO, NO2, PM2.5, SO2, and O3) were explored as potential drivers of sentiment. Results show that (1) the holiday sentiment was 1.31% higher than on weekends and 4.61% higher than on weekdays. Extreme precipitation, air pollution, and COVID-19 lockdown measures have reduced sentiment. (2) The sentiment in spring was found to be the highest. The numbers of functional zones with high sentiment values (>0.8) in spring were 13.59%, in summer 34.48%, and in autumn 14.71%. (3) Sentiment was highest under conditions of moderate greenness (0.4<NDVI<0.6) and comfortable daily temperature (25 °C < LST<30 °C). (4) Sentiment was negatively associated with daily air pollutants, such as PM2.5, NO2, and CO. This paper presents the effectiveness of sentiment quantification based on social media data and deep-learning techniques. The results provide practical implications and support decisions for sustainable urban health development.

Relationships between lightning occurrence and summer precipitation characteristics over china observed by GPM core observatory

The relationships between lightning occurrence and precipitation properties observed from the Dual-frequency Precipitation Radar and the Microwave Imager onboard the core observatory of the Global Precipitation Measurement Mission over China are investigated. The results indicate that lightning precipitation features (LPFs) typically account for <11% of the total precipitation features (PFs) over China, with the convective rain types accounting for ∼40%. The LPFs are more organized with wider spanning areas over Eastern China than Western China, and embedded with wider areas of convective cores than the non-lightning PFs. The probability of lightning (POL) increases linearly with the near-surface rain rate, liquid water path (LWP), and non-liquid water path (NLWP), while increases exponentially with the equivalent reflectivity factor (Ze). The precipitation profiles reveal that the maximum Ze values at higher altitudes are more sensitive to lightning occurrence than the ones at lower altitudes. The POL reaching above 80% mainly occurs when the areas of Ze above 20 dBZ or 30 dBZ exceed specific thresholds above the melting layer, while also occurs below the melting layer for the areas of Ze above 40 dBZ. The established precipitation parameter threshold-based lightning delineation scheme reveals that the maximum or minimum precipitation properties within PFs perform better than the mean ones in discriminating lightning. Furthermore, there is a great regional variance in the optimum thresholds of lightning delineation by precipitation properties over four typical regions of China. Overall, the present study may be helpful in the future prediction and nowcasting of lightning storms in numerical models.

Evaluation of Hourly Precipitation Characteristics from a Global Reanalysis and Variable-Resolution Global Model over the Tibetan Plateau by Using a Satellite-Gauge Merged Rainfall Product

High-resolution meteorological datasets are urgently needed for understanding the hydrological cycle of the Tibetan Plateau (TP), where ground-based meteorological stations are sparse. Rapid advances in remote sensing create possibilities to represent spatiotemporal properties of precipitation at a high resolution. In this study, the hourly precipitation characteristics over the TP from two gridded precipitation products, one from global reanalysis (the fifth generation of the European Center for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate; ERA5) and the other is simulated by Global-to-Regional Integrated forecast SysTem (GRIST) global nonhydrostatic model, are compared against satellite-gauge merged precipitation analysis (China Merged Precipitation Analysis; CMPA) from 27 July to 31 August 2014, and a satellite-retrieved precipitation estimate from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) is also evolved. Two aspects are mainly focused on: the spatial distribution and the elevation dependence of hourly precipitation characteristics (including precipitation amount, frequency, intensity, diurnal variations, and frequency–intensity structure). Results indicate that: (1) The precipitation amount, frequency, and intensity of CMPA and IMERG decrease with altitude in the Yarlung Tsangpo river valley (YTRV), but increase at first and then decrease with altitude (except for intensity) in the eastern periphery of TP (EPTP). ERA5 performed well on the variation of precipitation amount with altitude (especially in EPTP), but poorly on the frequency and intensity. GRIST is the antithesis of ERA5, but they all overestimate (underestimate) the frequency (intensity) at all heights; (2) With increasing altitude, the diurnal phase of precipitation of CMPA and IMERG shifted from night to evening in the two sub-regions. IMERG’s diurnal phase is 1 to 3 h earlier than CMPA’s, and the discrepancy decreases (increases) as the altitude increases in YTRV (EPTP). The diurnal phase of precipitation amount and frequency in ERA5 and GRIST is significantly earlier than CMPA, and the frequency peaks around midday except in the basin. GRIST’s simulation of the diurnal variation in intensity at various altitudes is consistent with CMPA; (3) ERA5 and GRIST overestimate (underestimate) the frequency of weak (intense) precipitation, with ERA5’s deviance being the most severe. The deviations increased with altitude. These findings provide intensive metrics to evaluate precipitation in complex terrain and are helpful for deepening the understanding of simulated biases for further improving performance in high-resolution simulation.

Investigating Hydroclimatic Variables Trends on the Natural Lakes of Western Greece Using Earth Observation Data.

Expected global climate change is allegedly becoming more intense, and the impacts on water resources are being tracked in various hydroclimatic regimes. The present research investigates a hydrologically important area of Greece, where four natural lakes are concentrated. It aims to quantify any potential long-term trends in lake water area, precipitation, and temperature timeseries. Water area timeseries spanning four decades are estimated by the mNDWI from Landsat satellite imagery and used as an index of each lake's water storage. Precipitation and temperature measurements are obtained from the open access datasets Hydroscope and ERA5-Land, respectively. All of the timeseries were tested seasonally and annually with the Pettitt and Mann-Kendal tests for statistically significant breakpoints and trends detection. No timeseries analysis resulted in a statistically significant (at 0.05 or 0.1 levels) annual or seasonal trend. The hydroclimatic regime over the past forty years in western Greece is found to have been relatively stable. Land use was also assessed to have been relatively unchanging, converging to the overall stability of the local water regime. However, the findings of this research should not be interpreted as a reassurance against climate change, but as a call to further research for the detailed regional and local assessment of climate change and hydroclimatic variability with acknowledged statistical approaches.

Evaluation of the GPM IMERG product at the hourly timescale over China

More-accurate satellite-based precipitation data are required for the study of hydrological and meteorological phenomena in the face of the rapidly changing climate. In this study, we evaluated IMERG V06 Final Precipitation product (IMERG-F) of the Global Precipitation Measurement (GPM) mission based on China Merged Precipitation Analysis (CMPA) data at the hourly timescale for the period from 2008 to 2017. Results indicated the following: (1) The spatial distribution of precipitation is highly consistent between the CMPA and IMERG product. However, precipitation and precipitation frequency ratios in the IMERG data are both overestimated, by 0.1–0.3 mm/h and 5%–15%, respectively. IMERG product has better ability to estimate precipitation in the east and south of China than in the west of China. The precipitation error is larger during nighttime than daytime, while the frequency ratio error is smaller during nighttime than daytime. (2) The time of maximum precipitation is more consistent between the two products than the time of maximum frequency ratio. The time of maximum precipitation is later in the IMERG product than in the CMPA product in most regions except northwestern China, and the time of maximum frequency ratio is later in most regions. (3) Gauge density has a stronger influence on the mean error in precipitation than the mean error in frequency ratio. The findings of this study could help improve the accuracy of satellite precipitation at the hourly scale and help it become a more important tool in the field of hydrometeorology.

Feasibility of satellite-based rainfall and soil moisture data in determining the triggering conditions of debris flow: The Jiangjia Gully (China) case study

Heavy precipitation is the main trigger for debris-flow hazards. The antecedent moisture condition, which is usually represented by antecedent precipitation, is another hydrometeorological contributing factor to debris flows that originate from shallow landslides. Satellite techniques are an economical and effective way to access rainfall and soil wetness information to determine the triggering conditions of debris flow. However, satellite-based thresholds need to be compared with ground-based thresholds and adjusted to the data source prior to their application. In this study, rainfall intensity-duration (I-D) thresholds were derived from the early and final run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG-E and IMERG-F) and gauge measurements using logistic regression for debris flows in the Jiangjia Gully, Yunnan Province, southern China, which has a dense rain gauge network. Data from both the IMERG-E and IMERG-F covered the entire study period. Our evaluation revealed that the I-D threshold derived from the IMERG-E deviated from the gauge-based thresholds. Although the threshold determined by the IMERG-F was comparable to the ground-based ones, the presence of substantial false positives and false negatives indicated that its performance was weaker than that of the gauge-based ones. Furthermore, the IMERG-F was suitable if the nearest available gauge was farther than 10 km from the debris-flow initiation zone. We evaluated the feasibility of the surface soil moisture product provided by the Climate Change Initiative program of the European Space Agency (CCI-SM), root-zone soil moisture derived from the CCI-SM using an exponential filter (SM-RZ), and antecedent precipitation for improving the performance of the thresholds by separately using them as the third explanatory variable in addition to rainfall intensity and duration in logistic regression. Satellite soil moisture data were available for 54% of the study period. The results suggested that including antecedent precipitation effectively improved the performance of the thresholds. In contrast, the performance of the thresholds increased only slightly when the CCI-SM or SM-RZ was included. Although these findings are valid only for this study area and need to be assessed for other regions, they present new insights for using satellite rainfall and soil moisture estimates to define thresholds for debris flow.

A Novel Merging Method for Generating High-Quality Spatial Precipitation Information over Mainland China

Accurate estimation of precipitation is critically important for a variety of fields, such as climatology, meteorology, and water resources. However, the availability of precipitation measurements has proved to be spatially inadequate for many applications. In this study, to acquire high-quality precipitation fields with enhanced accuracy and a fine-scale spatial resolution of 1 km × 1 km, we developed a new data fusion method by establishing an energy function model using the downscaled Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) precipitation product and high-density station observation in mainland China. Our merging approach was inspired by the interdisciplinary research framework integrating the methods in the fields of image processing, earth science, and machine learning. Cross-validation analyses were performed for the monthly precipitation over the period 2009–2018. It was found that the results of the newly developed method were more accurate than the original IMERG products in terms of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and Kling–Gupta efficiency (KGE). The merging precipitation results exhibit consistent spatial patterns with the original IMERG products, yet have good agreement with station observations. The gauge observations were the major source of the prediction skill of precipitation for the proposed method, and the downscaled-IMERG precipitation products added additional spatial details in the final merging results. Results indicate that the proposed merging method can reproduce the spatial details of the precipitation fields as well as enhance their accuracy. In addition, the time evolution of the error index indicates that the improvement in the merged result was stable over time, with KGE improving by 14% on average. The developed approach provides a promising way of estimating precipitation with high spatial resolution and high accuracy, which will benefit hydrological and climatological studies.

Analysis of the anomalous event GLE66 on October 29, 2003

Event GLE66 (29.10.2003) is a selected study in a series of three GLE events that occurred in the fall of 2003. there was this most powerful magnetic storm and a deep Forbush effect, during which GLE66 occurred. The appearance of the T-05 drilling model of the magnetosphere and the availability of a methodology debugged on dozens of less complex GLE events for solving the problem of achieving results for this complex event. Obtaining the values of the parameters of the cosmic ray flux at subsequent moments of time. A pattern typical of GLE events is observed: the spectrum of observed cosmic rays at the initial phase has an exponential shape, and then ends in a power law. The obtained spectra are in good agreement with measurements of cosmic ray precipitation on spacecraft at lower energies.

How Accurately Can Warm Rain Realistically Be Retrieved with Satellite Sensors? Part II: Horizontal and Vertical Heterogeneities

Abstract There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud-resolving model simulation of warm, shallow clouds is used to simulate satellite observations from three theoretical satellite architectures—one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beamfilling are very large, with retrieved rain rates negatively (low) biased by as much as 40%–50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% as compared with −28%, a reduced spread of retrieval errors, and an additional 17.5% of cases falling within desired uncertainty limits. The results speak to the need for additional high-resolution modeling simulations of warm rain so as to better characterize the uncertainties in satellite precipitation retrievals.

A Microphysics-Scheme-Consistent Snow Optical Parameterization for the Community Radiative Transfer Model

Abstract The satellite observational data assimilation community requires consistent hydrometer descriptions—including mass–size relation and particle size distribution—to be used in both the forecast model and observation operator. We develop a microphysics-scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass–size relation. Snow single-scattering properties are computed for four different mass–size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics-scheme-consistent snow bulk optical properties are derived. The Thompson-scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM), version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root-mean-square errors of 4.3, 13.0, 1.8, and 3.3 K in the 166-GHz (vertical polarization), 166-GHz (horizontal polarization), 183 ± 3-GHz (vertical polarization), and 183 ± 7-GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics-scheme-consistent snow optical parameterization in data assimilation applications.

Hydrological Model Evaluation of Ground, GPM IMERG, and CHIRPS precipitation data for Shabelle Basin in Ethiopia

Consistent and accurate precipitation measurement is a fundamental input component of the hydrological model. However, most developing countries, including Ethiopia, lack consistent and precise precipitation measurements. Nevertheless, satellitebased precipitation data may play a crucial role in bridging the data gap and providing precipitation inputs for rainfall-runoff models in regions with limited ground data. The study compares the ground precipitation data from the Ethiopian meteorological agency and SWALIM to the satellite-based GPM_IMERG and CHIRPS precipitation products. HECHMS software was used to model rainfall-runoff simulation, and the study area’s spatial characteristics and model development were processed with ArcGIS and HEC- GeoHMS. Since the data on hand is enough and fit for continuous event assessment, the deficit and constant loss method, a continuous event-based loss method, was tested, and found good model performance results. The onhand 14 years of precipitation and discharge data were divided into seven years for calibration and seven years for validation. Using Nash–Sutcliffe efficiency (ENS), coefficient of determination (R2), Root Mean Square Error (RMSE), and percentage bias objective functions, the performance of the satellite precipitation was evaluated. During calibration periods (Jan 2013 to Dec 2009), the model performance showed ENS values of 0.65, R2 values of 0.68, RMSE values of 0.6, a percent bias of 0.58% for the metrological stations, ENS values of 0.61, R2 values of 0.64, RMSE values of 0.6, a percent bias of 5.38% for CHIRPS, and ENS values of 0.63, R2 values of 0.66, RMSE values of 0.7, and a percent bias of 18.9% for GMP_IMERG satellite precipitation products. During validation periods (Jan 2010 to Dec 2016), the model performance showed ENS values of 0.75, R2 values of 0.78, RMSE values of 0.6, a percent bias of -16.9% for the metrological stations, ENS values of 0.71, R2 values of 0.74, RMSE values of 0.6, a percent bias of -22.12% for CHIRPS2, and ENS values of 0.76, R2 values of 0.76, RMSE values of 0.5, and a percent bias of -0.03% for GPM_IMERG satellite precipitation products. The study showed that the HEC-HMS model performed well and gave very good results for the hydrological model for the CHIRPS and GPM_IMERG rainfall products. The study also indicates that the model outperformed well during the validation period. Overall, the study found that the simulated GPM_IMERG product gave better results than the simulated CHIRPS product.

Moisture Sources and Pathways Determine Stable Isotope Signature of Himalayan Waters in Nepal

AbstractThe Himalayan mountain range produces one of the steepest and largest rainfall gradients on Earth, with &gt;3 m/yr rainfall difference over a ∼100 km distance. The Indian Summer Monsoon (ISM) contributes more than 80% to the annual precipitation budget of the central Himalayas. The remaining 20% falls mainly during pre‐ISM season. Understanding the seasonal cycle and the transfer pathways of moisture from precipitation to the rivers is crucial for constraining water availability in a warming climate. However, the partitioning of moisture into the different storage systems such as snow, glacier, and groundwater and their relative contribution to river discharge throughout the year remains under‐constrained. Here, we present novel field data from the Kali Gandaki, a trans‐Himalayan river, and use 4‐year time series of river and rain water stable isotope composition (δ18O and δ2H values) as well as river discharge, satellite Global Precipitation Measurement amounts, and moisture source trajectories to constrain hydrological variability. We find that rainfall before the onset of the ISM is isotopically distinct and that ISM rain and groundwater have similar isotopic values. Our study lays the groundwork for using isotopic measurements to track changes in precipitation sources during the pre‐ISM to ISM transition in this key region of orographic precipitation. Specifically, we highlight the role of pre‐ISM precipitation, derived from the Gangetic plain, to define the seasonal river isotopic variability across the central Himalayas. Lastly, isotopic values across the catchment document the importance of a large well‐mixed groundwater reservoir supplying river discharge, especially during the non‐ISM season.