000			04132nab a22004457a 4500
003			EC-PaCDF
005			20250314083738.0
008			250314s2025 xxu|||||||||||||||||engxd
040			_aEC-PaCDF _bspa _cEC-PaCDF
041			_aeng
082	0	4	_222 _a333.7317
100	1		_aTurini, Nazli. _eautor
245	1	0	_aOperational satellite cloud products need local adjustment – The Galapagos case of ecoclimatic cloud zonation / _cNazli Turini ...[et al.].
260	3		_bElsevier B.V., _c2025.
300			_a18 p. ; _bilus. col., tablas y mapas.
546			_aLike many small oceanic islands, the Galapagos archipelago, renowned for its unique geographic location and exceptional endemic biodiversity, faces significant challenges under climate change. In particular, the atmospheric water supply for the ecosystem and the local population is under threat, with clouds and rain playing an important role in ensuring freshwater availability under climate change. Better planning of adaptation measures would require climate data on clouds as a prerequisite for precipitation and rainfall at high spatio-temporal resolution, which are not available in this area. Operational products such as satellite derived cloud and precipitation products or reanalysis data are widely used to compensate for the lack of local data availability but are often poorly suited for regional applications. In the current study, we aim to generate high quality area-wide cloud information to distinguish ecoclimatic cloud zones that may require different adaptation measures to climate change. To address this issue, we have developed a new physical rule-based cloud mask retrieval specifically tailored for the Galapagos Archipelago, based on data from the third generation GOES-16 Advanced Baseline Imager (ABI) geostationary satellite. The new Galapagos Rainfall Retrieval (GRR) cloudmask was tested against independent observational data and compared to both the operational GOES-16 ACM (ABI Clear sky Mask) and the MODIS cloudmask benchmark cloud mask. Our test results confirm that the GRR-cloudmask (Probability of Detection POD = 0.94, Critical Success Index CSI = 0.92–0.93) clearly outperforms the operational ACM-cloudmask (POD = 0.56–0.68, CSI = 0.55–0.67). Area-wide tests against the MODIS cloud mask showed a CSI of 0.72 and a POD of 0.74 for the ACM, which is superior to the GOES-16 ACM-cloudmask. We produced cloud frequency maps for all months and day slots and analysed cloud frequency using ancillary meteorological data. In general, the cool season (Jun-Dec) / night shows much higher cloud frequencies than the warm season (Jan-May) / daytime. However, regional cloud patterns differ along a west-to-east and south-to-north gradient, depending on complex interactions of forcing parameters such as exposure to the main circulation, sea surface temperature zones, altitude and land cover. A k-mean cluster analysis resulted in nine ecoclimatic cloud zones over land, which are much more differentiated than the widely used four-zone classification. The results will help to develop more site-specific climate change adaptation planning for the iconic Galapagos National Park.
550			_a2673
653			_aGeostationary Operational Environmental
653			_aMedio ambiente operativo geoestacionario
653			_aSatellite-16
653			_aSatélite-16
653			_aThreshold-based
653			_aBasado en umbrales
653			_aCloud mask
653			_aMáscara de nube
653			_aGalapagos Archipelago
653			_aArchipiélago de Galápagos
700	1		_aDelgado Maldonado, Byron. _eautor
700	1		_aZander, Samira. _eautor
700	1		_aBayas López, Steve Darwin. _eautor
700	1		_aBallari, Daniela. _eautor
700	1		_aCélleri, Rolando. _eautor
700	1		_aOrellana Alvear, Johanna. _eautor
700	1		_aSchmidt, Benjamin. _eautor
700	1		_aScherer, Dieter. _eautor
700	1		_aBendix, Jörg. _eautor
773			_gVolume 315, 1 April 2025, 107918. _tAtmospheric Research
856			_uhttps://doi.org/10.1016/j.atmosres.2025.107918
942			_2ddc _cARTICLE
999			_c16195 _d16195