scholarly journals Development of an Urban High-Resolution Air Temperature Forecast System for Local Weather Information Services Based on Statistical Downscaling

Atmosphere ◽  
2018 ◽  
Vol 9 (5) ◽  
pp. 164 ◽  
Author(s):  
Chaeyeon Yi ◽  
Yire Shin ◽  
Joon-Woo Roh
Keyword(s):  
High Resolution ◽  
Air Temperature ◽  
Statistical Downscaling ◽  
Information Services ◽  
Forecast System ◽  
Weather Information ◽  
Temperature Forecast ◽  
Local Weather

scholarly journals Short-to-medium range hydrologic forecast to manage water and agricultural resources in India

2016 ◽  
Author(s):  
Reepal Shah ◽  
Atul Kumar Sahai ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Root Zone ◽  
Tropical Meteorology ◽  
Forecast System ◽  
Temperature Forecast ◽  
Ensemble Mean ◽  
Medium Range ◽  
Prediction Systems ◽  
Hydrologic Forecast

Abstract. Water resources and agriculture are often affected by the weather anomalies in India resulting in a disproportionate damage. While short to medium range prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), Global Ensemble Forecast System (GEFSv2) and four products from Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from IITM) can be used operationally to provide hydrologic forecast in India at 7–45 days lead time. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Forecast based on the IITM-ensemble mean showed better skill in majority of India for all the lead times (7–45 days) in comparison to the other forecast products. Moreover, the VIC simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe droughts years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

scholarly journals Tracing Hybridity in the Provision of ICT-Enabled Agricultural Weather Information Services in Ghana

2021 ◽  
pp. 1-31
Author(s):  
Rebecca Sarku ◽  
Erik van Slobbe ◽  
Katrien Termeer ◽  
Rebecca Chudaska ◽  
Agatha Siwale ◽  
...  
Keyword(s):  
Information Services ◽  
Weather Information

scholarly journals An application of statistical downscaling to estimate surface air temperature in Japan

2002 ◽  
Vol 107 (D10) ◽  
pp. ACL 14-1-ACL 14-10 ◽  
Author(s):  
Naoko Oshima ◽  
Hisashi Kato ◽  
Shinji Kadokura
Keyword(s):  
Air Temperature ◽  
Statistical Downscaling ◽  
Surface Air Temperature

scholarly journals A High-Resolution National-Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model

2016 ◽  
Vol 52 (4) ◽  
pp. 950-964 ◽  
Author(s):  
Alan D. Snow ◽  
Scott D. Christensen ◽  
Nathan R. Swain ◽  
E. James Nelson ◽  
Daniel P. Ames ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
National Scale ◽  
Forecast System ◽  
Hydrologic Forecast

COMPARATIVE ANALYSIS OF SPECTRA OF THE 2000-YEAR NORTHERN HEMISPHERE AVERAGE SURFACE AIR TEMPERATURE RECONSTRUCTIONS

2021 ◽  
pp. 5-13
Author(s):  
N. M. DATSENKO ◽  
◽  
D. M. SONECHKIN ◽  
B. YANG ◽  
J.-J. LIU ◽  
...  
Keyword(s):  
High Resolution ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Wavelet Transforms ◽  
Spectral Composition ◽  
Low Frequency ◽  
Surface Air Temperature ◽  
Temporal Variations ◽  
Continuous Wavelet Transforms ◽  
Stable Estimation

The spectral composition of temporal variations in the Northern Hemisphere mean surface air temperature is estimated and compared in 2000-year paleoclimatic reconstructions. Continuous wavelet transforms of these reconstructions are used for the stable estimation of energy spectra. It is found that low-frequency parts of the spectra (the periods of temperature variations of more than 100 years) based on such high-resolution paleoclimatic indicators as tree rings, corals, etc., are similar to the spectrum of white noise, that is never observed in nature. This seems unrealistic. The famous reconstruction called “Hockey Stick” is among such unrealistic reconstructions. Reconstructions based not only on high-resolution but also on low-resolution indicators seem to be more realistic, since the low-frequency parts of their spectra have the pattern of red noise. They include the “Boomerang” reconstruction showing that some warm periods close to the present-day one were observed in the past.

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.

Aerosol Effects on Temperature Forecast in the COSMO-Ru Model

2021 ◽  
pp. 29-39
Author(s):  
A. A. Poliukhov ◽  
◽  
D. V. Blinov ◽  
◽  
Keyword(s):  
Air Temperature ◽  
Numerical Experiments ◽  
Surface Air Temperature ◽  
European Part ◽  
Clear Sky ◽  
Temperature Forecast ◽  
European Part Of Russia ◽  
Simulated Surface ◽  
Aerosol Effects ◽  
Sky Conditions

Aerosol effects on the forecast of surface temperature, as well as temperature at the levels of 850 and 500 hPa over Europe and the European part of Russia are studied using various aerosol climatologies: Tanre, Tegen, and MACv2. The numerical experiments with the COSMO-Ru model are performed for the central months of the seasons (January, April, July, and October) in 2017. It is found that a change in the simulated surface air temperature over land can reach 1C when using Tegen and MACv2 data as compared to Tanre. At 850 and 500 hPa levels, the changes do not exceed 0.4C. At the same time, it is shown that a decrease in the root-mean-square error of 2-m air temperature forecast at individual stations reaches 0.5C when using Tegen and MACv2 data and 1C for clear-sky conditions in Moscow.

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