This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1° × 1° gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)‐South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA‐3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller “missing” values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root‐mean‐square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small‐scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

Evaluation of Global Precipitation Measurement‐Integrated Multi‐satellitE Retrieval for GPM (GPM‐IMERG) final precipitation product is performed over Japan, Nepal, and Philippines regions against further improved APHRODITE‐2 V1801R1 product. The evolution is carried out for nearly two consecutive years 2014–2015. Various qualitative and quantitative statistical indices such as mean bias, root‐mean‐square error, correlation coefficient, false alarming ratio, and probability of detection are considered to evaluate GPM‐IMERG precipitation estimates with APHRODITE‐2. Intraseasonal variability of two products is shown to explore the seasonal dependency of GPM‐IMERG performance. The performance of GPM‐IMERG research product with respect to rainfall intensity is shown by the cumulative probability distribution of target and reference data sets. Percentile‐based statistics is implemented for evaluating the advantages of GPM‐IMERG over Tropical Rainfall Measuring Mission‐3B42 while detecting the light and heavy rainfall events during wet/dry seasons. The overall performance of GPM‐IMERG seems to be good over Japan followed by Philippines and Nepal regions. This feature is clearly evidenced in terms of mean bias, root‐mean‐square error, and correlation magnitudes over three regions. GPM‐IMERG shows ability to follow the intraseasonal variability as shown by APHRODITE‐2 product with minor differences observed in precipitation maximum values during rainy season. Good agreement is seen between GPM‐IMERG and APHRODITE‐2 at different rainfall intensities except underestimation during heavy rainfall events. GPM‐IMERG seems to be improved in detecting light/heavy rainfall event magnitude than TRM‐3B42. However, the performance of both data sets encountered clear dependency on seasons.

Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and subdaily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area of 50 km × 50 km with an intergauge distance of 6-12 km. Morphological features of rainfall in two principal rainy seasons (southwest monsoon, SWM, and northeast monsoon, NEM) show marked differences. The NEM rainfall exhibits significant spatial variability and most of the rainfall is associated with large-scale/long-lived systems (during wet spells), whereas the contribution from small-scale/short-lived systems is considerable during the SWM. Rain events with longer duration and copious rainfall are seen mostly in the western quadrants (a quadrant is 1/4 of the study region) in the SWM and northern quadrants in the NEM, indicating complex spatial variability within the study region. The diurnal cycle also exhibits large spatial and seasonal variability with larger diurnal amplitudes at all the gauge locations (except for 1) during the SWM and smaller and insignificant diurnal amplitudes at many gauge locations during the NEM. On average, the diurnal amplitudes are a factor of 2 larger in the SWM than in the NEM. The 24 h harmonic explains about 70 % of total variance in the SWM and only ∼ 30 % in the NEM. During the SWM, the rainfall peak is observed between 20:00 and 02:00 IST (Indian Standard Time) and is attributed to the propagating systems from the west coast during active monsoon spells. Correlograms with different temporal integrations of rainfall data (1, 3, 12, 24 h) show an increase in the spatial correlation with temporal integration, but the correlation remains nearly the same after 12 h of integration in both monsoon seasons. The 1 h resolution data show the steepest reduction in correlation with intergauge distance and the correlation becomes insignificant after ∼ 30 km in both monsoon seasons.Validation of high-resolution rainfall estimates from various MPEs against the gauge rainfall data indicates that all MPEs underestimate the light and heavy rain. The MPEs exhibit good detection skills of rain at both 3 and 24 h resolutions; however, considerable improvement is observed at 24 h resolution. Among the different MPEs investigated, the Climate Prediction Centre morphing technique (CMORPH) performs better at 3-hourly resolution in both monsoons. The performance of Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA) is much better at daily resolution than at 3-hourly, as evidenced by better statistical metrics than the other MPEs. All MPEs captured the basic shape of the diurnal cycle and the amplitude quite well, but failed to reproduce the weak/insign...

Characteristics of raindrop size distribution (DSD) obtained by Global Precipitation Measurement (GPM) mission dual‐frequency precipitation radar (DPR) are assessed over Gadanki region during southwest (SW) and northeast (NE) monsoon seasons utilizing 2 years (2014–2015) of DSD measurements by an impact‐type disdrometer. The mass weighted mean diameter (Dm in mm) and normalized DSD scaling parameter for concentration (Nw in mm−1 m−3) show pronounced seasonal differences at low to medium rain rates in the disdrometer data, in accordance with the previous studies, but not in the GPM‐DPR data. Similar features are observed every year in disdrometer measurements and over different spatial domains in GPM‐DPR measurements, indicating that sampling mismatch errors are insignificant. The reasons for the absence of seasonal differences in DSDs derived from GPM‐DPR are examined by simulating the reflectivities at Ku‐ and Ka‐bands, utilizing the disdrometer measurements and T‐matrix scattering indices. Results suggest that the Dm and Nw retrieved from single‐frequency and dual‐frequency algorithms utilizing the disdrometer data also show seasonal differences in accordance with the observations with under and overestimation of Dm and Nw, respectively. When compared with the disdrometer, the Dm values retrieved from the GPM‐DPR (official products) are severely underestimated at high rain rates (R > 8 mm h−1) during the SW monsoon season. On the other hand, during low rain rates (R < 8 mm h−1), a slight underestimation (overestimation) of Dm is seen during the SW (NE) monsoon. The mean Nw values retrieved from GPM‐DPR agree poorly with disdrometer data during both the monsoon seasons.