Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Kirstetter, Pierre‐Emmanuel; Petersen, Walter A.; Kummerow, Christian D.; Wolff, David B.

doi:10.1007/978-3-030-35798-6_7

Cited by 16 publications

(14 citation statements)

References 66 publications

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“…Among the biases in the profile magnitude, a substantial portion of the EPC's underestimation for the convective cases in the middle-to-high latitudes can be attributed to the error in the representation of the precipitation type in the algorithms. It has been pointed out by previous studies that the representation error of precipitation type is one of the causes of the passive MW algorithm's estimation error for surface precipitation rate (Petković et al 2019;Kirstetter et al 2020). The result of this study suggests that the estimation performance of the vertical precipitation profiles is also affected by the representation error in precipitation type.…”

Section: Summary and Concluding Remarksmentioning

confidence: 55%

“…Such error may be mitigated by the separation technique of the convective and stratiform precipitation developed for the passive MW algorithms (e.g., Petković et al 2019). It should be noted that the precipitation type information provided by the CMB product, which is used as a reference in this study, may have an error itself (Kirstetter et al 2020). The analyses using other sources of information, such as ground-based observations, are left for the future study.…”

Section: Summary and Concluding Remarksmentioning

confidence: 99%

“…A ground-based surface precipitation rate over the continental United States and surrounding water by the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) ground radar data, projected to the GMI footprints (Kirstetter et al 2012(Kirstetter et al , 2018, is also used for the validation of the surface precipitation rate. The precipitation rate by the GV-MRMS is available at the GMI footprints for each GPM Core Observatory satellite overpass.…”

Section: Reference Datasetsmentioning

confidence: 99%

“…validating the full profile is that the 3D volumes sampled by scanning ground-based radars and space-based measurements are not aligned, and the ground radar scans do not fully capture the full top-to-bottom cloud structure. On the other hand, surface precipitation data are more widely collected and disseminated, e.g., the Ground Validation Multi-Radar Multi-Source (GV-MRMS) data in the United States (Kirstetter et al 2012). Spaceborne radar observations are an exception, as these can provide the vertical structure of precipitation on a global scale.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals

Utsumi

Turk

Haddad

et al. 2021

Journal of Hydrometeorology

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Precipitation estimation based on passive microwave (MW) observations from low Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the Emissivity Principal Components (EPC) algorithm and the Goddard Profiling Algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20 - 50% in the middle-to-high latitudes, while GPROF overestimates it by about 20 - 50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

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Section: Summary and Concluding Remarksmentioning

confidence: 55%

Section: Summary and Concluding Remarksmentioning

confidence: 99%

Section: Reference Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals

Utsumi

Turk

Haddad

et al. 2021

Journal of Hydrometeorology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, the representativeness of any deterministic assessment of model RH is confined to the time and space domain over which it is performed, with limited extension over other regimes, regions, seasons, etc. These issues are not confined to the study of RH but are, to an extent, common to those of all geophysical variables (see for instance Kirstetter et al, 2020, for a discussion on precipitation).…”

Section: Introductionmentioning

confidence: 99%

Novel assessment of numerical forecasting model relative humidity with satellite probabilistic estimates

et al. 2022

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Abstract. A novel method of comparison between an atmospheric model and satellite probabilistic estimates of relative humidity (RH) in the tropical atmosphere is presented. The method is developed to assess the Météo-France numerical weather forecasting model ARPEGE (Action de Recherche Petite Echelle Grande Echelle) using probability density functions (PDFs) of RH estimated from the SAPHIR (Sondeur Atmosphérique du Profil d'Humidité Intertropicale par Radiométrie) microwave sounder. The satellite RH reference is derived by aggregating footprint-scale probabilistic RH to match the spatial and temporal resolution of ARPEGE over the April–May–June 2018 period. The probabilistic comparison is discussed with respect to a classical deterministic comparison confronting each model RH value to the reference average and using a set confidence interval. This study first documents the significant spatial and temporal variability in the reference distribution spread and shape. We demonstrate the need for a finer assessment at the individual case level to characterize specific situations beyond the classical bulk comparison using determinist “best” reference estimates. The probabilistic comparison allows for a more contrasted assessment than the deterministic one. Specifically, it reveals cases where the ARPEGE-simulated values falling within the deterministic confidence range actually correspond to extreme departures in the reference distribution, highlighting the shortcomings of the too-common Gaussian assumption of the reference, on which most current deterministic comparison methods are based.

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Long-Term High-Resolution Gauge Adjusted Satellite Rainfall Product over India

Kumar

Varma

Kubota

et al. 2022

Preprint

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This study aims to create a 21-year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland. The targeted resolutions of the GSMaP are hourly and 0.1°× 0.1°. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) daily gauge analysis (0.5°× 0.5°) and Indian Meteorological Department (IMD) daily gridded rainfall product (0.25°× 0.25°) were utilized to generate two long-term rainfall products, GSMaP CPC and GSMaP IMD rainfall, respectively. After preliminary verification of the GSMaP CPC and GSMaP IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon (ISM) periods of 2000-2020 with comparisons of other merged rainfall products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). The results suggest GSMaP IMD has a smaller root-mean-square difference (RMSD) and higher correlation than GSMaP CPC, evaluated against independent rainfall products. In the threehour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP IMD with respect to GSMaP CPC throughout the day. The statistics against the hourly dense rain gauge network in Karnataka suggests that the GSMaP IMD is more effective in capturing large spatiotemporal rainfall variation over India. Thus, validation results with the independent sources suggest that GSMaP IMD rainfall generally improved over GSMaP CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

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Integrated Multi-satellite Evaluation for the Global Precipitation Measurement: Impact of Precipitation Types on Spaceborne Precipitation Estimation

Cited by 16 publications

References 66 publications

Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals

Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals

Novel assessment of numerical forecasting model relative humidity with satellite probabilistic estimates

Long-Term High-Resolution Gauge Adjusted Satellite Rainfall Product over India

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