The Convective Rainfall Rate from Cloud Physical Properties Algorithm for Meteosat Second-Generation Satellites: Microphysical Basis and Intercomparisons using an Object-Based Method

Tapiador, Francisco J.; Marcos, Cecilia; Sancho, Juan Manuel

doi:10.3390/rs11050527

Cited by 12 publications

(8 citation statements)

References 43 publications

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“…The basis for the generation of the satellite-based precipitation field from the Meteosat observations is software delivered by the NWC SAF (Satellite Application Facilities on Support to Nowcasting and Very Short Range Forecasting), which is a program of EUMETSAT (the European Organisation for the Exploitation of Meteorological Satellites) (http://www.nwcsaf.org/). This software generates, inter alia, products related to precipitation [53].…”

Section: Measurements (Meteosat) and Nwc Saf Processingmentioning

confidence: 99%

Quality-Based Combination of Multi-Source Precipitation Data

et al. 2020

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A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

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Section: Measurements (Meteosat) and Nwc Saf Processingmentioning

confidence: 99%

Quality-Based Combination of Multi-Source Precipitation Data

et al. 2020

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“…It is well understood that deeper convection can yield heavier rain (e.g., Adler & Mack, 1984;Song et al, 2020). In particular the cloud depth is often inferred from infrared cloud top brightness temperatures (e.g., So & Shin, 2018); and the cloud depth is often used to estimate convective rain rate (e.g., Tapiador et al, 2019). Here we use satellite observations to show a declining trend of TC CF.…”

Section: Introductionmentioning

confidence: 99%

Has there been a recent shallowing of tropical cyclones?

Lai

Toumi

2023

Preprint

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Many aspects of tropical cyclone (TC) properties at the surface have been changing but any systematic vertical changes are unknown. Here we document a recent trend of high thick clouds of TCs. The global inner-core high thick cloud fraction measured by satellite has decreased from 2002 to 2021 by about 10% per decade. The TC inner-core surface rain rate is also found to have decreased during the same period by a similar percentage. This suppression of high thick clouds and rain has been largest during the intensification phase of the strongest TCs. Hence, these two independent and consistent observations suggest that the TC inner-core convection has weakened and that TCs have become shallower recently at least. For this period the lifetime maximum intensity of major TCs has not changed and this suggests an increased efficiency of the spin-up of TCs.

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“…These include products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) [13,14] and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN) [15][16][17]. Furthermore, these also include products from a Climate Prediction Center Morphing Technique (CMORPH) [18][19][20], the Global Satellite Mapping of the Precipitation (GSMaP) project [21][22][23], the convective rainfall rate from cloud physical properties (CRPh) algorithm [24], and the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) algorithm [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

Ren

et al. 2021

Remote Sensing

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In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

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The Convective Rainfall Rate from Cloud Physical Properties Algorithm for Meteosat Second-Generation Satellites: Microphysical Basis and Intercomparisons using an Object-Based Method

Cited by 12 publications

References 43 publications

Quality-Based Combination of Multi-Source Precipitation Data

Quality-Based Combination of Multi-Source Precipitation Data

Has there been a recent shallowing of tropical cyclones?

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

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