Evaluation of gridded precipitation datasets over Madagascar

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10453407" target="_blank" >RIV/00216208:11320/22:10453407 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=j69wRKj1bP" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=j69wRKj1bP</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/joc.7628" target="_blank" >10.1002/joc.7628</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of gridded precipitation datasets over Madagascar

Popis výsledku v původním jazyce

Madagascar is among the countries whose agriculture is heavily dependent on rainfall. However, the country lacks accurate and reliable early warning systems for droughts and floods, partly due to insufficient station rainfall data. The purpose of this study is to identify rainfall datasets that can complement observation data by appraising 15 datasets (gauge-based, reanalysis, and satellite estimates). The study compares the temporal and spatial performance of datasets at annual and seasonal scales during 1983-2015. In all the analyses, CHIRPS presents lower biases, so it is chosen as the reference data in the Taylor diagram for the final evaluation analysis. Even though ranking datasets is neither possible nor appropriate since each dataset performs differently throughout each analysis, some datasets show reasonable consistency. This is the case with MSWEP, ERA5, and UDEL. On the other hand, MERRA2, CMAP, and TAMSAT are least preferred for use due to their considerable biases (specifically TAMSAT during the dry season). CRU, PRECL, ERAINT, CFSR, and JRA55 also present some degrees of deficiencies at either annual or seasonal scales. These findings are crucial for any future rainfall analysis over the country in order to minimize inaccuracy in monitoring rainfall.

Název v anglickém jazyce

Evaluation of gridded precipitation datasets over Madagascar

Popis výsledku anglicky

Madagascar is among the countries whose agriculture is heavily dependent on rainfall. However, the country lacks accurate and reliable early warning systems for droughts and floods, partly due to insufficient station rainfall data. The purpose of this study is to identify rainfall datasets that can complement observation data by appraising 15 datasets (gauge-based, reanalysis, and satellite estimates). The study compares the temporal and spatial performance of datasets at annual and seasonal scales during 1983-2015. In all the analyses, CHIRPS presents lower biases, so it is chosen as the reference data in the Taylor diagram for the final evaluation analysis. Even though ranking datasets is neither possible nor appropriate since each dataset performs differently throughout each analysis, some datasets show reasonable consistency. This is the case with MSWEP, ERA5, and UDEL. On the other hand, MERRA2, CMAP, and TAMSAT are least preferred for use due to their considerable biases (specifically TAMSAT during the dry season). CRU, PRECL, ERAINT, CFSR, and JRA55 also present some degrees of deficiencies at either annual or seasonal scales. These findings are crucial for any future rainfall analysis over the country in order to minimize inaccuracy in monitoring rainfall.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Climatology

ISSN

0899-8418

e-ISSN

1097-0088

Svazek periodika

42

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

19

Strana od-do

7028-7046

Kód UT WoS článku

000782149200001

EID výsledku v databázi Scopus

2-s2.0-85127544934