Downscaling of Climate Information

2014 ◽  
pp. 201-250 ◽  
Author(s):  
Linda O. Mearns ◽  
Melissa S. Bukovsky ◽  
Sarah C. Pryor ◽  
Victor Magaña
Keyword(s):  
Climate Information

On the application of the Unified Model to produce finer scale climate information for New Zealand

2002 ◽  
Vol 22 ◽  
pp. 19 ◽  
Author(s):  
Bhaskaran ◽  
Renwick ◽  
Mullan
Keyword(s):  
New Zealand ◽  
Unified Model ◽  
Climate Information

scholarly journals To what extent do weather and climate information services drive the adoption of climate-smart agriculture practices in Ghana?

2021 ◽  
pp. 100309
Author(s):  
Abdoulaye Djido ◽  
Robert B. Zougmoré ◽  
Prosper Houessionon ◽  
Mathieu Ouédraogo ◽  
Issa Ouédraogo ◽  
...  
Keyword(s):  
Information Services ◽  
Climate Information ◽  
Weather And Climate ◽  
Smart Agriculture

scholarly journals Multidisciplinary ENVIROMIS conferences as a tool connecting climate information producers and users

2016 ◽  
Vol 48 ◽  
pp. 012001 ◽  
Author(s):  
E P Gordov ◽  
V N Lykosov ◽  
Yu E Gordova ◽  
E Yu Genina
Keyword(s):  
Climate Information

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

European scale climate information services for water use sectors

2015 ◽  
Vol 528 ◽  
pp. 503-513 ◽  
Author(s):  
Michelle T.H. van Vliet ◽  
Chantal Donnelly ◽  
Lena Strömbäck ◽  
René Capell ◽  
Fulco Ludwig
Keyword(s):  
Water Use ◽  
Information Services ◽  
Climate Information

A Real-Time Climate Information System for the Midwestern United States

1990 ◽  
Vol 71 (11) ◽  
pp. 1601-1609 ◽  
Author(s):  
Kenneth E. Kunkel ◽  
Stanley A. Changnon ◽  
Carl G. Lonnquist ◽  
James R. Angel
Keyword(s):  
United States ◽  
Information System ◽  
Real Time ◽  
Climate Information ◽  
Midwestern United States

Analog Models for Empirical-Statistical Downscaling

2021 ◽  
Author(s):  
María Laura Bettolli
Keyword(s):  
Statistical Downscaling ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Weather Conditions ◽  
Climate Information ◽  
Analog Method ◽  
Weather Typing ◽  
Regional Climates ◽  
Local Weather

Global climate models (GCM) are fundamental tools for weather forecasting and climate predictions at different time scales, from intraseasonal prediction to climate change projections. Their design allows GCMs to simulate the global climate adequately, but they are not able to skillfully simulate local/regional climates. Consequently, downscaling and bias correction methods are increasingly needed and applied for generating useful local and regional climate information from the coarse GCM resolution. Empirical-statistical downscaling (ESD) methods generate climate information at the local scale or with a greater resolution than that achieved by GCM by means of empirical or statistical relationships between large-scale atmospheric variables and the local observed climate. As a counterpart approach, dynamical downscaling is based on regional climate models that simulate regional climate processes with a greater spatial resolution, using GCM fields as initial or boundary conditions. Various ESD methods can be classified according to different criteria, depending on their approach, implementation, and application. In general terms, ESD methods can be categorized into subgroups that include transfer functions or regression models (either linear or nonlinear), weather generators, and weather typing methods and analogs. Although these methods can be grouped into different categories, they can also be combined to generate more sophisticated downscaling methods. In the last group, weather typing and analogs, the methods relate the occurrence of particular weather classes to local and regional weather conditions. In particular, the analog method is based on finding atmospheric states in the historical record that are similar to the atmospheric state on a given target day. Then, the corresponding historical local weather conditions are used to estimate local weather conditions on the target day. The analog method is a relatively simple technique that has been extensively used as a benchmark method in statistical downscaling applications. Of easy construction and applicability to any predictand variable, it has shown to perform as well as other more sophisticated methods. These attributes have inspired its application in diverse studies around the world that explore its ability to simulate different characteristics of regional climates.

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