Multisite simulation of daily precipitation and temperature in the Rhine Basin by nearest-neighbor resampling

2001 ◽  
Vol 37 (11) ◽  
pp. 2761-2776 ◽  
Author(s):  
T. Adri Buishand ◽  
Theo Brandsma
Keyword(s):  
Nearest Neighbor ◽  
Daily Precipitation ◽  
Rhine Basin ◽  
Precipitation And Temperature

scholarly journals Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

2013 ◽  
Vol 17 (6) ◽  
pp. 2147-2159 ◽  
Author(s):  
E. P. Maurer ◽  
T. Das ◽  
D. R. Cayan
Keyword(s):  
Bias Correction ◽  
General Circulation ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Circulation Model ◽  
The United States ◽  
Historical Period ◽  
Late 20Th Century ◽  
Precipitation And Temperature ◽  
Gcm Biases

Abstract. When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme values and for different sets of historical years. Daily precipitation and maximum and minimum temperature from late 20th century simulations by four GCMs over the United States were compared to gridded observations. Using random years from the historical record we select a "base" set and a 10 yr independent "projected" set. We compare differences in biases between these sets at median and extreme percentiles. On average a base set with as few as 4 randomly-selected years is often adequate to characterize the biases in daily GCM precipitation and temperature, at both median and extreme values; 12 yr provided higher confidence that bias correction would be successful. This suggests that some of the GCM bias is time invariant. When characterizing bias with a set of consecutive years, the set must be long enough to accommodate regional low frequency variability, since the bias also exhibits this variability. Newer climate models included in the Intergovernmental Panel on Climate Change fifth assessment will allow extending this study for a longer observational period and to finer scales.

Deep Learning for Daily Precipitation and Temperature Downscaling

2021 ◽  
Author(s):  
Fang Wang ◽  
Di Tian ◽  
Lisa Lowe ◽  
Latif Kalin ◽  
John Lehrter
Keyword(s):  
Deep Learning ◽  
Daily Precipitation ◽  
Temperature Downscaling ◽  
Precipitation And Temperature

A Versatile Weather Generator for Daily Precipitation and Temperature

2012 ◽  
Vol 55 (3) ◽  
pp. 895-906 ◽  
Author(s):  
J. Chen ◽  
F. P. Brissette ◽  
R. Leconte ◽  
A. Caron
Keyword(s):  
Daily Precipitation ◽  
Weather Generator ◽  
Precipitation And Temperature

Trends in extreme daily precipitation and temperature near lake Hövsgöl, Mongolia

2007 ◽  
Vol 27 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Banzragch Nandintsetseg ◽  
J. Scott Greene ◽  
Clyde E. Goulden
Keyword(s):  
Daily Precipitation ◽  
Lake Hovsgol ◽  
Precipitation And Temperature
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