Validation of the final monthly Integrated Multi-satellitE Retrievals for GPM (IMERG) Version 05 and Version 06 with ground-based precipitation gauge measurements across the Canadian Arctic

To assess the performance of the most recent versions of the Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), namely V05 and V06, in Arctic regions, comparisons with Environment and Climate Change Canada (ECCC) Climate Network stations north of 60°N were performed. This study focuses on the IMERG monthly final products. The mean bias and mean error-weighted bias were assessed in comparison with twenty-five precipitation gauge measurements at ECCC Climate Network stations. The results of this study indicate that IMERG generally detects higher precipitation rates in the Canadian Arctic than ground-based gauge instruments, with differences ranging up to 0.05 mm h−1 and 0.04 mm h−1 for the mean bias and the mean error-weighted bias, respectively. Both IMERG versions perform similarly, except for a few stations, where V06 tends agree slightly better with ground-based measurements. IMERG’s tendency to detect more precipitation is in good agreement with findings indicating that weighing gauge measurement suffer from wind undercatch and other impairing factors, leading to lower precipitation estimates. Biases between IMERG and ground-based stations were found to be slightly larger during summer and fall, which is likely related to the increased precipitation rates during these seasons. Correlations of both versions of IMERG with the ground-based measurements are considerably lower in winter and spring than during summer and fall, which might be linked to issues that Passive Microwave (PMW) sensors encounter over ice and snow. However, high correlation coefficients with medians of 0.75-0.8 during summer and fall are very encouraging for potential future 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.

Satellite-Based Precipitation Estimates Validation Using Surface Stations in the Central Region of the State of São Paulo, From 1981 to 2019

With the advance of remote sensing technologies, meteorological satellites have become an alternative in the process of monitoring and measuring meteorological variables, both spatially and temporally. The present study brings some additional elements to the validation of satellite-based precipitation estimates by evaluating the CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station) monthly product for the central region of the state of São Paulo, Brazil, in the period 1981-2019. Initially, the general relationship between satellite estimates and surface rainfall data is assessed using the linear adjustment and error analysis in both temporal and spatial perspectives, followed by a trend analysis using Laplace test. The monthly map analysis showed a better performance of CHIRPS during the dry period (April to August) than for the wet period (October to March). Finally, monthly trends showed, in general, the same pattern of variability in rainfall over 38 years and a prevalence toward the reduction of rainfall. In summary, CHIRPS product seems a reasonable alternative for regions that lack historical rainfall information.

A Spatial Downscaling Methodology for GRACE Total Water Storage Anomalies Using GPM IMERG Precipitation Estimates

A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG). Considering that the major driving force for changes in TWS is precipitation, we tested our hypothesis that coarse resolution, i.e., 1°, GRACE TWSA can be effectively downscaled to 0.1° using GPM IMERG data. The algorithm for the downscaling process comprises the development of a regression equation at the coarse resolution between the GRACE and GPM IMERG data, which is then applied at the finer resolution with a subsequent residual correction procedure. An ensemble of GRACE data from three processing centers, i.e., GFZ, JPL and CSR, was used for the time period from June 2018 until March 2021. To verify our downscaling methodology, we applied it with GRACE data from 2005 to 2015, and we compared it against modeled TWSA from two independent datasets in the Thrace and Thessaly regions in Greece for the same period and found a high performance in all examined metrics. Our research indicates that the downscaled GRACE observations are comparable to the TWSA estimated with hydrological modeling, thus highlighting the potential of GRACE data to contribute to the improvement of hydrological model performance, especially in ungauged basins.

A novel high-resolution gridded precipitation dataset for Peruvian and Ecuadorian watersheds – development and hydrological evaluation

A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (for ‘Rain for Peru and Ecuador’), at 0.1° spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of a) the random forest method to merge multi-source precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and b) observed and modeled streamflow data to firstly detect biases and secondly further adjust gridded precipitation by inversely applying the simulated results of the eco-hydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Paċific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low-, high- and peak-flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.

Wintertime precipitation climate of Central Southwest Asia: Diagnostics and forecasting

<p>Central Southwest Asia (CSWA; 20°–47°N, 40°–85°E) is a water-scarce and a societally vulnerable region, prone to significant variations in precipitation during the winter months of November–April. Wintertime precipitation variations have a direct impact on CSWA's water resources, agricultural productivity, energy use, and human society. Because of the close relationship between climate and human well-being, an improved understanding of winter season precipitation and its variability over CSWA is of critical importance. However, due to multiple regional challenges (e.g. socio-political instability, extreme topographical heterogeneity, poor coverage of in situ stations, and others) analysis of precipitation in this region has been limited. In an attempt to bridge the existing knowledge gap, this thesis aims to advance our understanding of CSWA's wintertime precipitation climate through three separate, but inter-related studies on 1) evaluation of multi-source gridded precipitation dataset, 2) investigation of spatial and temporal patterns of precipitation and its links with large-scale modes of climate variability, 3) development of a statistical forecast model. Additionally, precipitation evaluation is also relevant to the overlapping and important region of the Indian subcontinent; a detailed seasonal analysis for which is also presented. First, the performance of several commonly used gridded precipitation products from multiple sources: gauge-based, satellite-derived, and reanalysis is analysed for all four seasons over the Indian Subcontinent. Results show that the degree of uncertainty in all precipitation estimates varies by region (e.g. topographic relief) and the type of precipitation (e.g. convective, orographic). At the seasonal scale, satellite-products perform better, while reanalyses generally overestimate precipitation. Greater discrepancies occur in areas with low gauge densities, owing to which a complete understanding of the accuracy and limitations of precipitation estimates is hampered for the northwestern region of the Indian subcontinent. In an extension study, ten multi-source precipitation products are evaluated against an ensemble of four gauge-only datasets. This analysis is carried out for CSWA, which also includes the northwestern region of the Indian subcontinent. Spatial and temporal analysis of results shows that GPCC is a suitable observational dataset for studying long-term wintertime precipitation variations over CSWA. The satellite-derived TRMM 3B42-V7 is a potentially reliable alternative to gauge measurements, while the performance of MERRA reanalysis is satisfactory. Further, the spatial-temporal patterns of wintertime precipitation variability over CSWA are explored. Three leading patterns are identified by empirical orthogonal function (EOF) analysis, and the associated time series are related to global SST and other large-scale atmospheric circulation fields. The leading patterns of winter precipitation are significantly linked with the El Niño–Southern Oscillation (ENSO); East Atlantic–Western Russia (EA-WR); Siberian High; North Pacific Oscillation (NPO); Scandinavian pattern; and the long-term warming of the Indian Ocean SST. The inter-decadal change of relationship between the first-mode of winter precipitation and ENSO is also investigated, which shows that CSWA precipitation variability was closely related to the extratropical EA-WR (tropical ENSO) teleconnection before (after) the 1980's. Finally, the level and origin of seasonal forecast skill of wintertime precipitation anomalies over CSWA are examined using the statistical method of canonical correlation analysis (CCA). The preceding months’ (September–October) SST is used as predictors, and CCA experiments are performed for two sets of time periods, 1950/51–2014/15 and 1980/81–2014/15. For both prediction periods, the potential source of predictability originates largely from SST variations related to ENSO and the Pacific Decadal Oscillation (PDO). A higher (lower) correlation skill of 0.71 (0.38) is obtained between observations and cross-validated precipitation forecasts for the period 1980/81–2014/15 (1950/51–2014/15); which shows that ENSO played a dominant role in creating skillful predictions for CSWA wintertime precipitation in recent years.</p>