Satellite Precipitation Estimates Research Articles

Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.

A new pixel-to-object method for evaluating the capability of the GPM IMERG product to quantify precipitation systems

One of the major advantages of satellite precipitation estimates is that they can provide a complete picture of precipitation systems; a difficult task to achieve using sparsely distributed rain gauges. This advantage greatly enhances our understanding of natural precipitation. Also, for this reason, it is difficult to know satellite-derived precipitation system performance using a direct “object-to-object” method involving ground-truth reference data. To solve this issue, we develop a broadly applicable method, namely, a “pixel-to-object” method, which regards discrete gauges as sampling gridboxes to evaluate a satellite precipitation product from the precipitation system perspective. Approximately 46,000 AWSs recorded hourly data in China are used to evaluate the IMERG precipitation product. The results show that the smaller the precipitation systems are, the higher the false alarm proportion, the lower the miss proportion, and the more underestimated the precipitation rates are. The miss and false alarm proportions near the precipitation system boundaries are much larger than those far away from the boundaries, while the precipitation rate bias presents a negative-positive–negative bias pattern from the boundaries to the centers of precipitation systems. These results indicate the strong influences of the precipitation system scale and position in the systems on satellite product biases. A universal overestimation of precipitation system sizes is revealed, which is more severe for small systems than for large systems and in the afternoon than in the morning. The different data sources in the IMERG product throughout a day evidently influence the delineation accuracy of precipitation system size in a day.

Can satellite precipitation estimates capture the magnitude of extreme rainfall Events?

ABSTRACT With the increasing frequency of extreme rainfall events, the likelihood of rainfall disasters is also increasing, presenting serious risks of casualties and economic losses. Therefore, exploring the capability of Satellite-based Precipitation Products (SPPs) to capture the magnitudes of extreme rainfall events is significant for preventing precipitation-related disasters. Selecting ten heavy rainfall events in wet seasons over China, this study evaluated the performance of mainstream SPPs from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) in determining the extreme values of heavy rainfall events. The main findings are as follows: (1) SPPs significantly underestimated the magnitudes of extreme rainfalls events, with the most underestimated and closest values for certain events reaching only one-tenth and 50% of ground station values, respectively; (2) the evaluation of the top 1% hourly precipitation presented similar evident errors, with mean relative bias (BIAS) and root mean square error (RMSE) of approximately −80% and 40 mm hour−1; (3) the spatiotemporal patterns of IMERG-Final were closer to those of the ground station and GSMaP-Now captured more maximum values of the ten events. Nevertheless, the accuracy of these SPPs is still not sufficient for the analysis of extreme rain disasters.

Evaluating the performance of multiple satellite-based precipitation products in the Congo River Basin using the SWAT model

Study regionCongo River Basin Study FocusPrecipitation is the major driving force of hydrological processes and is one of the main input datasets for hydrological models. The Congo River Basin is one of the least studied major river basins in the world and suffers from the scarcity and difficulty in accessing rain gauge data, necessitating the use of satellite precipitation estimates for hydrological studies. In this study, we have made a comprehensive comparison of different satellite-based precipitation products’ inputs for a hydrological model in the Congo River Basin. New hydrological insight for this regionOur findings showed that the products based on satellite-only source tend to overestimate the rainy season peaks in comparison with the 3B42_V7 product. On the other hand, the satellite products that consider gauge calibration presented better agreements between each other. The hydrological model was able to reproduce the general precipitation products characteristics, while the gauge-adjusted satellite products performed better than those without gauge adjustments. Overall precipitation patterns have a crucial effect on the model’s performance and leads to different streamflow and water balance components’ values. The choice of rainfall product has a significant importance in the interpretation of the simulated hydrological cycle.

Evaluation of GSMaP and Trmm Monthly Rainfall Satellite Data in Wadi Ahin, Sohar Area, Sultante of Oman

This research compares the accuracy of Forecasting Global Precipitation Measurement (GPM)-derived satellite precipitation estimates (SPEs) for Wadi Ahin in Sohar area in Sultanate of Oman with Tropical Rainfall Measurement Mission (TRMM). To do so, GSMaP version 7 and TRMM data sets were evaluated at their regional 0.10 and 0.250 spatial resolution respectively. Both the wet and dry seasons were assessed on a monthly basis for each month. A grid cell with two gauges, one ground and the other GSMAP or TRMM, is calculated with the CC, the %RMSE, and the %B for all the considered datasets. Statistical measurements are provided here in more detail. The monthly analysis takes into account the whole period, dry season, and wet season separately for Mean regional and Mean spatial. Verification of monthly rainfall at a spatial level shows that GSMaP underestimates with a correlation coefficient, 0.704, bias -7.88% and RMSE 117.4%. Verification with the regional level shows that GSMAP performs well with a correlation coefficient of higher than 0.9. On the other hand, analysis of TRMM data sets shows a good verification in the spatial level and low accuracy in the regional level where the selected gauges are far from each other. The results indicate the need for improvement of GSMaP and TRMM estimates by doing some combinations between the datasets before application in this wadi.

Analysis of ENSO-Driven Variability, and Long-Term Changes, of Extreme Precipitation Indices in Colombia, Using the Satellite Rainfall Estimates CHIRPS

Climate change includes the change of the long-term average values and the change of the tails of probability density functions, where the extreme events are located. However, obtaining average values are more straightforward than the high temporal resolution information necessary to catch the extreme events on those tails. Such information is difficult to get in areas lacking sufficient rain stations. Thanks to the development of Satellite Precipitation Estimates with a daily resolution, this problem has been overcome, so Extreme Precipitation Indices (EPI) can be calculated for the entire Colombian territory. However, Colombia is strongly affected by the ENSO (El Niño—Southern Oscillation) phenomenon. Therefore, it is pertinent to ask if the EPI’s long-term change due to climate change is more critical than the anomalies due to climate variability induced by the warm and cold phases of ENSO (El Niño and La Niña, respectively). In this work, we built EPI annual time series at each grid-point of the selected Satellite Precipitation Estimate (CHIRPSv2) over Colombia to answer the previous question. Then, the Mann-Whitney-Wilcoxon test was used to compare the samples drawn in each case (i.e., change tests due to both long-term and climatic variability). After performing the analyses, we realized that the importance of the change depends on the region analyzed and the considered EPI. However, some general conclusions became evident: during El Niño years (La Niña), EPI’s anomaly follows the general trend of reduction -drier conditions- (increase; -wetter conditions-) observed in Colombian annual precipitation amount, but only on the Pacific, the Caribbean, and the Andean region. In the Eastern plains of Colombia (Orinoquía and Amazonian region), EPI show a certain insensitivity to change due to climatic variability. On the other hand, EPI’s long-term changes in the Pacific, the Caribbean, and the Andean region are spatially scattered. Still, long-term changes in the eastern plains have a moderate spatial consistency with statistical significance.

QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests

AbstractAccurate and reliable near‐real‐time satellite precipitation estimation is of great importance for operational large‐scale flood forecasting and drought monitoring. The state‐of‐the‐art precipitation post‐processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P‐NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near‐real‐time predictors to further improve the model performance. Using the Integrated Multi‐satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG‐E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG‐E and is also superior to the bias‐corrected product (IMERG Final Run, IMERG‐F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG‐E, raw IMERG‐E, and IMERG‐F using ground observation show that the corrected IMERG‐E improves correlation coefficients (0.7), mean error (−0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG‐E (0.31, −0.72 and 5.5 mm/day) and IMERG‐F (0.34, −0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P‐NRT improves on the various deficiencies of the raw IMERG‐E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post‐processing for near‐real‐time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.

A Two-Step Approach to Blending GSMaP Satellite Rainfall Estimates with Gauge Observations over Australia

An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful; the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia.

Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin

Precipitation is the main input for hydrological models. However, due to limitations of rain gauge stations, satellite precipitation estimates have become a good alternative to precipitation information. In this context, this study aimed to validate the precipitation data with Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data, in addition to assessing the uncertainty and performance of the Soil and Water Assessment Tool (SWAT) using observed precipitation (OP), TMPA, and IMERG data. Statistical coefficients were used to validate TMPA and IMERG precipitation data. P-factor and r-factor were considered for the uncertainty analysis, while the Nash-Sutcliffe efficiency (NSE), its logarithmic version (LNSE), and the percent bias (PBIAS) were analyzed to characterize the model performance analysis in monthly time steps. There was an overestimation by TMPA and IMERG in the precipitation estimation, especially in the dry period. OP, TMPA, and IMERG setups presented satisfactory results for uncertainty and performance analysis in hydrological modeling. The IMERG setup generally showed better results than the TMPA setup, being a good alternative for hydrological modeling, especially in regions with scarce precipitation datasets.

Correcting the bias of daily satellite precipitation estimates in tropical regions using deep neural network

The high spatiotemporal variability of rainfall in tropical regions has posed a great challenge for generating satisfactory satellite precipitation products (SPPs). Most of previous studies have found a modest performance of various SPPs in estimating daily rainfall in tropical regions such as Malaysia. In-depth research on effective ways to correct the bias of SPPs in the tropical region is urgently needed. This study aims to establish a bidirectional long short-term memory recurrent network (Bi-LSTM) framework for the bias correction of SPPs, and apply it to correct daily rainfall estimates of the early runs of Integrated Multisatellite Retrievals for the Global Precipitation Measurement (IMERG-E) from 2010 to 2016 in the Kelantan River Basin, Malaysia. After optimization and statistical comparison, Bi-LSTM with the covariates of daily maximum and minimum temperature (Bi-LSTM-T) was determined to be the best model for bias correction. Annually, Bi-LSTM-T could raise the correlation coefficient (CC) of IMERG-E by 26.7%, while reducing its root mean square error (RMSE) and mean absolute error (MAE) by 23.9% and 21.7% in the Kelantan River Basin. In the four seasons, it increased the CC of IMERG-E by 19.7–27.5%, while decreasing its RMSE and MAE by 18.4–30.0% and 20.9–23.2%. Multiple statistical tests confirmed that Bi-LSTM-T significantly outperformed two benchmark methods, namely ratio bias correction (RBC) and cumulative distribution function (CDF) matching, in correcting the bias of IMERG-E for all four seasons. This suggests that the Bi-LSTM-T model may work as a promising framework of great potentials for correcting the bias of SPPs in tropical regions, where adequate precipitation data are in great need for diverse purposes such as water-related disaster prevention and mitigation.

Spatiotemporal analysis of drought by CHIRPS precipitation estimates

Drought is one of the most devastating natural hazards causing considerable losses in all climatic zones of the world. It is one of the most complex and the least understood hazards at the same time because of its spatially heterogeneous and temporally variable character. Spatially dense and uniformly distributed ground-based meteorological data are needed for proper spatial and temporal drought analysis. In practice, such data are lacking in general due either to the nonexistence of ground stations or their uneven and scarce distribution over a region. This creates a great potential in the use of satellite precipitation estimates (SPEs) such as the long-period high-resolution Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data in drought analysis. In this study, we aim to analyze drought over the Kucuk Menderes River Basin in the western part of Turkey by using the CHIRPS data, which were found highly correlated with precipitation in the local ground stations. The analysis was performed by considering the spatial variability and temporal change in the drought characterization based on the Standardized Precipitation Index (SPI) calculated at the 3-month (seasonal) timescale. Drought in the river basin was found to have a within-year variability from month to month, and a spatial variability over the basin in any given month. Also, an over-year variability with a decreasing trend exists, which could be considered a signal for more strengthened droughts in the future. The study eventually demonstrates how the CHIRPS SPEs could be useful in the spatial and temporal drought analysis for regions with limited ground-based meteorological data.

From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?

With the end of the Tropical Rainfall Measuring Mission (TRMM) era, its successor of Global Precipitation Measurement (GPM) aims to provide a new generation of satellite precipitation estimates (SPE) with higher spatiotemporal resolution and wider spatial coverage. As the representative precipitation retrieval algorithms for GPM and TRMM eras, it is necessary to compare the Integrated Multi-satellite Retrievals for GPM (IMERG) with the TRMM Multi-satellite Precipitation Analysis (TMPA) to investigate the improvements of SPEs from TRMM to GPM. This study analyzes the post/near-real-time SPEs from TMPA and IMERG over Mainland China from April 2014 to March 2018 at daily scale, using precipitation observations from 510 meteorological stations as reference. A very comprehensive evaluation system, which contains four indicators of continuous metrics, categorical metrics, error components, and systematic and random errors is adopted in this study. Besides, four patterns with spatial distribution, temporal pattern, precipitation intensity distribution, and overall metrics are applied to illustrate each indicator's performance. The results show that IMERG products have lower RB and RMSE and higher CC than TMPA and greatly reduce the three error components (hit bias, missed precipitation, and false precipitation). Furthermore, IMERG products present a higher capability to capture the precipitation events correctly and generally have lower systematic and random errors than TMPA products. However, there are still rooms for IMERG products to improve, such as higher false alarm ratio (FAR) and more false precipitation of IMERG products at relatively low rain rates, the 25% higher systematic error of IMERG-E (IMERG “Early Run” near-real-time SPE) compared to TMPA-RT (TMPA near-real-time SPE). In addition, we find the gauge adjustment from GPCC is not a very effective way to improve the detecting capability and reduce the systematic errors for near-real-time SPEs over Mainland China. This study provides some valuable information on the transition from TMPA to IMERG, which may advance the algorithm's development and the hydrometeorological applications.

Ground Validation of GPM IMERG-F Precipitation Products with the Point Rain Gauge Records on the Extreme Rainfall Over a Mountainous Area of Sumatra Island

Accurate satellite precipitation estimates over areas of complex topography are still challenging, while such accuracy is of importance to the adoption of satellite data for hydrological applications. This study evaluated the ability of Integrated Multi-satellitE Retrievals for GPM -Final (IMERG) V06 product to observe the extreme rainfall over a mountainous area of Sumatra Island. Fifteen years of optical rain gauge (ORG) observation at Kototabang, West Sumatra, Indonesia (100.32°E, 0.20°S, 865 m above sea level), were used as reference surface measurement. The performance of IMERG-F was evaluated using 13 extreme rain indexes formulated by the Expert Team on Climate Change Detection and Indices (ETCCDI). The IMERG-F overestimated the values of all precipitation amount-based indices (PRCPTOT, R85P, R95P, and R99P), three precipitation frequency-based indices (R1mm, R10mm, R20mm), one precipitation duration-based indices (CWD), and one precipitation intensity-based indices (RX5day). Furthermore, the IMERG-F underestimated the values of precipitation frequency-based indices (R50mm), one precipitation duration-based indices (CDD), one precipitation intensity-based indices (SDII). In terms of correlation, only five indexes have a correlation coefficient (R) > 0.5, consistent with Kling–Gupta Efficiency (KGE) value. These results confirm the need to improve the accuracy of the IMERG-F data in mountainous areas.

An evaluation of the performance of IMERG hourly precipitation estimates in a geographical sub-region with complex terrain and climate conditions: a case study in the upper Red River Basin

ABSTRACT This study comprehensively evaluates four satellite precipitation estimates (SPE) of the version 6 (V06) of Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) at hourly scale in the upper Red River Basin (URRB), located in Yunnan, by applying 12 rain gauges during the rainy season (May to October) from 2019 to 2020. Four SPE includes IMERG-E, IMERG-L, IMERG-FU and IMERG-FC, with IMERG-FC is calibrated by the Global Precipitation Climatology Centre (GPCC) gauge. Due to the complex terrain of the URRB, we divide the URRB into three subregions. The evaluation considers precipitation detection capability (POD, FAR and CSI), correlation (CC), error situation (RMSE and RBIAS) and the quantitative analysis index (CPI). The results show that (1) among these SPE, IMERG-FC always has higher POD and FAR, better correlation and larger RBIAS. (2) In the URRB, the results produced by all SPE are overestimates compared to ground-based observations (GBO). And, the detection capability of IMERG is better in the low precipitation threshold but worsens with the increasing precipitation thresholds. (3) IMERG shows worse performance in the dry-hot valley than in the other two areas. (4) The performance of IMERG-L is better than IMERG-FC and other SPE in the URRB.

A Novel Real-Time Error Adjustment Method With Considering Four Factors for Correcting Hourly Multi-Satellite Precipitation Estimates

High-accuracy near-real-time satellite precipitation estimates (SPEs) provide an opportunity for hydrometeorologists to improve the forecasting of extreme events, such as flood, landslide, tropical cyclone, and other extreme events, at the large scale. However, the currently operational near-real-time SPEs still have larger errors and uncertainties. In this study, we found that there exists a clear relationship of spatial plane function (SPF) between retrieval errors of SPEs and four crucial factors including topography, seasonality, climate type, and rain rate. Based on this finding, we proposed a novel error adjustment method to correct the near-real-time hourly global satellite mapping of precipitation (GSMaP-NRT) estimates in real-time. The new satellite precipitation dataset, namely, ILSF-RT, was then inter-compared with the latest near-real-time GSMaP product suite (i.e., GSMaP-NRT and GSMaP-Gauge-NRT). Verification results show that the proposed method can effectively reduce the retrieval errors of GSMaP-NRT for various terrains and rain rates over different seasons and climate-type areas. The new ILSF-RT even exhibits a general improvement over the GSMaP-Gauge-NRT estimates. Furthermore, one important merit of the new method is that it can perform rather well in validation even when not much historical data were applied as training samples in calibration, for example, during the generation of ILSF-RT, only 45 data pairs of satellite retrievals and ground observations were used for winter season over Chinese arid areas. However, the results of bias score show that the current method seems unsuitable to adjust the rainfall events with higher rain rates (>=1 mm hr^−1), which needs to be further improved.

Cascaded Downscaling–Calibration Networks for Satellite Precipitation Estimation

Precipitation is a critical process in the terrestrial hydrological circulation, affecting climate change, water resource management, and agricultural production. Satellite-borne observations have prominent advantages in macro and mesoscopic quantitative precipitation estimation. Nevertheless, they are subject to low spatial resolution and inherent biases. Therefore, this study utilizes the surface-surface downscaling network and point-surface fusion network for fine-resolution and high-precision precipitation mapping over China. To deeply explore the complicated relationships between various ancillary factors, ground measurements and satellite precipitation, an attention mechanism based convolutional network (AMCN) is used for spatial downscaling and a geo-intelligent deep belief network (Geoi-DBN) is used for ground-satellite fusion. Experimental results indicate that cascaded networks toward two different objectives are superior to baseline methods, achieving R2 and RMSE of about 0.84 and 27.23 mm/month, respectively. Besides, the assistance of geo-intelligent items and ancillary factors contributes to fusion accuracy. This study provides an effective way for precipitation estimation over China.

Changes in rain and snow over the Tibetan Plateau based on IMERG and Ground-based observation

The latest generation of satellite precipitation estimates, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Version 06 (IMERG V06), calibrated by monthly rain gauge data, is applied to investigate the changing characteristics of precipitation in different phases (rain, snow, and sleet) over the Tibetan Plateau (TP) from 2001 to 2020. The performance of the IMERG product in capturing the characteristics of the three precipitation phases is firstly evaluated against the ground-based observation. The results show that the IMERG product performs well in capturing the spatial distribution, magnitudes, and annual cycle of the amount and frequency of precipitation in different phases. The bias of annual precipitation amount (frequency) averaged in the 90 stations used in this study is approximately 10% (20%). Comparatively, the IMERG product shows higher skill in estimating rain than snow, and performs better in the other three seasons than in winter. In addition, the performance of the IMERG product is comparable in the southern and northern TP. Both the annual amount and frequency of precipitation have increased in most parts of the TP in the last two decades, with maximal values of 50 mm and 20 days per decade, respectively, occurring in the central part of eastern TP. Changes in the amount and frequency of precipitation are dominated by rain and snow, respectively, in the study period. In addition, light and moderate precipitation contribute approximately 70% and 90% to the increases of the regional mean precipitation amount and frequency, respectively, in the central part of eastern TP. There are no significant and uniform changes in the snowy season during the last two decades, therefore, the increase in snowy days in most areas of the TP is caused by more frequent snow events in the fairly constant snowy season.

Evaluation of GPM IMERG and its constellations in extreme events over the conterminous united states

Improved quantification of extreme precipitation rates using observations has far-reaching implications for environmental sciences, especially for hydrometeorological studies. Yet, uncertainties still remain in satellite precipitation estimates, especially for a merged product. This study evaluates the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) in extreme events over the conterminous US. Three approaches are followed to define and evaluate extreme events: (1) a percentile-based analysis, (2) an event-based analysis using the National Weather Service storm database, and (3) a frequency-based analysis using intensity–durationfrequency (IDF) curves. The IMERG Early Run (ER), Late Run (LR), and Final Run (FR) products and their original passive microwave and infrared (IR) sensors are intercompared against the National Centers for Environmental Predictions Stage IV ground-based radar precipitation data from 2015 to 2019. In particular, we break down the performance in three types of events (rain, snow, and hail). The results reveal that: (1) three types of extreme definitions converge toward an overall agreement - the degrees of underestimation of high-end extreme precipitation rates increases with data latency (FR > LR > ER) and FR delivers overall best performance; (2) passive microwave (PMW) estimates generally exhibits better detectability and quantification of extreme precipitation than IR estimates, especially in heavy rains; (3) Amongst PMW sensors, MHS (SAPHIR)-based estimates show the best (worst) extreme detection with CSI (Critical Success Index) equaling 0.15 (0.10) while AMSR and SSMIS outperform others for quantifying extreme rates. Lastly, different sensors (e.g., imagers and sounders in PMW and IR) deliver variable performance regarding different precipitation types. These findings reveal that IMERG is not a homogeneous precipitation product when it comes to estimating precipitation extremes. There are rooms for improvement to enhance homogeneity across precipitation estimates used in IMERG.

Meso‐ and microscale response to variation in cloudiness at three forested sites in the Maritime Continent

AbstractThis study examines the relationships between the diurnal precipitation cycle, local morning insolation, and local and large‐scale moisture availability using local radiative flux and precipitation observations from three forested tropical sites, together with ERA5 reanalysis data. Our results suggest that, away from the coast, there is a weak positive correlation between local morning insolation and the magnitude of the afternoon precipitation peak, and a weak negative relationship between locally observed surface moisture in the morning and the magnitude of the afternoon precipitation peak. There is little relationship between large‐scale column moisture and the local afternoon precipitation peak. We examine analogous results in convection‐permitting simulations. Results suggest that the simulated afternoon precipitation rate shows almost no dependence on surface radiative forcing or moisture availability, while the observed afternoon precipitation rate is dominated by radiative forcing. Simulated and observed satellite precipitation and cloud estimates for a single case study suggest a diversity of surface‐flux–precipitation relationships amongst model configurations.

The Role of Mesoscale Convective Systems in Precipitation in the Tibetan Plateau Region

AbstractMesoscale convective systems (MCSs) have been identified as an important source of precipitation in the Tibetan Plateau (TP) region. However, the characteristics and structure of MCS‐induced precipitation are not well understood in this location. Infrared (IR) satellite imagery has been used for MCS tracking, but cirrus clouds or cold surfaces can lead to false MCS classification over mountain regions. Here, we combine brightness temperatures from IR imagery with satellite precipitation estimates from GPM IMERG and track MCSs over the TP, at the boundary of the TP (TPB), and in the surrounding lower‐elevation plains (LE), between 2000 and 2019. In most parts of LE and TPB, MCSs produced 50%–80% of the total summer precipitation (60%–90% of summer heavy precipitation), whereas MCSs over the TP account for below 10% of the total summer precipitation (10%–30% of summer heavy precipitation). Our results also show that MCSs that produce the largest amounts of heavy precipitation are characterized by longevity and large extents rather than by high intensities. These are mainly located in the populous areas south and east of the TP. A tracking of meso‐β convective systems over the TP shows that small‐scale convection makes a large contribution to total and heavy precipitation. This suggests that more localized convective systems are important for the regional water cycle over the higher terrain and highlights the importance of convective‐scale modeling to improve our understanding of precipitation dynamics in the TP region.