scholarly journals A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006

2008 ◽  
Vol 113 (D20) ◽  
Author(s):  
M. R. Haylock ◽  
N. Hofstra ◽  
A. M. G. Klein Tank ◽  
E. J. Klok ◽  
P. D. Jones ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Gridded Data ◽  
Data Set ◽  
Temperature And Precipitation

scholarly journals Comparing the Köppen-Geiger, Feddema, and UNEP Climate Classifications: An Application to Iran

2021 ◽  
Author(s):  
Tayeb Raziei
Keyword(s):  
High Resolution ◽  
Satisfactory Agreement ◽  
Pairwise Comparison ◽  
Combined Effect ◽  
Sea Surface ◽  
Gridded Data ◽  
Northern Iran ◽  
Data Set ◽  
Climate Maps ◽  
Semi Arid

Abstract This study introduces the climates of Iran defined by Köppen-Geiger, Feddema’s, and UNPEP classifications that applied to a high-resolution ground-based gridded data set relative to the 1985–2017 period. Ten Köppen-Geiger climate types were found for Iran, from which Bwh, Bsk, Csa, Bsh, and Bwk cumulatively account for more than 98% of the territory. Likewise, from 36 possible Feddema’s climate types, Iran possesses fifteen climate types from which the Dry Cool, Semiarid Torrid, Semiarid Hot, Semiarid Warm, Dry warm, Semiarid Cool, and Moist Cool climates collectively occupied approximately 93% of the country. Similarly, arid, semi-arid, humid, and sub-humid UNEP climate types characterized more than 98% of Iran. A few other vertically stratified climates appeared at the highlands of Iran just because of changes in elevation and slope aspects of the mountains. The combined effect of topography and vicinity to sea also creates very distinct climate types in northern Iran. The climate maps of the three used methods reflect the joint effects of topography, latitudinal variation, and land/sea surface contrast on the climate of Iran. A pairwise comparison made between the three classifications showed a satisfactory agreement between the three schemes in representing the main climate types of Iran.

scholarly journals Validation of Surface Temperature Derived From MERRA‐2 Reanalysis Against IMD Gridded Data Set Over India

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Priyanshu Gupta ◽  
Sunita Verma ◽  
R. Bhatla ◽  
Amit Singh Chandel ◽  
Janhavi Singh ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Gridded Data ◽  
Data Set

scholarly journals A European daily high-resolution observational gridded data set of sea level pressure

2011 ◽  
Vol 116 (D11) ◽  
Author(s):  
E. J. M. van den Besselaar ◽  
M. R. Haylock ◽  
G. van der Schrier ◽  
A. M. G. Klein Tank
Keyword(s):  
High Resolution ◽  
Sea Level ◽  
Gridded Data ◽  
Data Set ◽  
Sea Level Pressure ◽  
Level Pressure

scholarly journals Decadal surface temperature trends in India based on a new high-resolution data set

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Robert S. Ross ◽  
T. N. Krishnamurti ◽  
Sandeep Pattnaik ◽  
D. S. Pai
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Data Set ◽  
High Resolution Data ◽  
Temperature Trends ◽  
Resolution Data

scholarly journals Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set

Water ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 500 ◽  
Author(s):  
Chee Wong ◽  
Juneng Liew ◽  
Zulkifli Yusop ◽  
Tarmizi Ismail ◽  
Raymond Venneker ◽  
...  
Keyword(s):  
High Resolution ◽  
Peninsular Malaysia ◽  
Gridded Data ◽  
Data Set ◽  
Rainfall Characteristics

scholarly journals Spatio-temporal variability of daily precipitation concentration in Spain based on a high-resolution gridded data set

2017 ◽  
Vol 38 ◽  
pp. e518-e530 ◽  
Author(s):  
R. Serrano-Notivoli ◽  
J. Martín-Vide ◽  
M. A. Saz ◽  
L. A. Longares ◽  
S. Beguería ◽  
...  
Keyword(s):  
High Resolution ◽  
Temporal Variability ◽  
Daily Precipitation ◽  
Gridded Data ◽  
Data Set ◽  
Precipitation Concentration ◽  
Spatio Temporal

scholarly journals Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

2015 ◽  
Vol 7 (1) ◽  
pp. 1-17 ◽  
Author(s):  
M. Riffler ◽  
G. Lieberherr ◽  
S. Wunderle
Keyword(s):  
Time Series ◽  
Surface Water ◽  
High Resolution ◽  
Surface Temperature ◽  
Water Temperature ◽  
Lake Surface ◽  
Data Set ◽  
Simulation Based ◽  
Very High

Abstract. Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record (1989–2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of −0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and −0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ~ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.

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