scholarly journals PreciPatch: A Dictionary-based Precipitation Downscaling Method

2020 ◽  
Vol 12 (6) ◽  
pp. 1030 ◽  
Author(s):  
Mengchao Xu ◽  
Qian Liu ◽  
Dexuan Sha ◽  
Manzhu Yu ◽  
Daniel Q. Duffy ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Super Resolution ◽  
Global Climate Models ◽  
Weather Data ◽  
Downscaling Method ◽  
Spatially Distributed ◽  
Local Impact ◽  
Global And Local ◽  
Precipitation Events

Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).

scholarly journals Application of prognostic ensembles of global climate models to generate distributed estimates of water content in catchment basins

2021 ◽  
Vol 289 ◽  
pp. 01009
Author(s):  
Valeriya Petruhina
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
Water Content ◽  
Human Impact ◽  
Climate Models ◽  
Global Climate ◽  
The State ◽  
Global Climate Models ◽  
Spatially Distributed ◽  
Long Time

The problem of predicting climate change and its impact on humans is quite important and relevant in recent times. For a long time, mechanisms and methods for predicting the behavior of the climate in various regions and regions of our planet have been developed. Due to climate change, aggressive human impact on nature, and other various factors, the methods developed in the mid-twentieth century are becoming ineffective, and it is time-consuming but feasible to calculate using several methods. The article considers the technology of processing geoclimatic data, which is used to form spatially distributed predictive estimates of the state of the atmosphere.

scholarly journals Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

2021 ◽  
Vol 2069 (1) ◽  
pp. 012070
Author(s):  
C N Nielsen ◽  
J Kolarik
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Dynamical Downscaling ◽  
Global Climate ◽  
Global Climate Models ◽  
Weather Data ◽  
Building Performance ◽  
Comprehensive Overview ◽  
Local Climate ◽  
The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

scholarly journals Evaluating the effect of the statistical downscaling method on monthly precipitation estimates of global climate models

2021 ◽  
Keyword(s):  
Climate Models ◽  
Prediction Models ◽  
Global Climate ◽  
Linear Regression Analysis ◽  
Pressure Level ◽  
Global Climate Models ◽  
Specific Humidity ◽  
Monthly Precipitation ◽  
Estimation Errors ◽  
Downscaling Method

<p>Researches to foresee the possible effects of climate change on the environment and living beings for taking necessary precautions on time have increased in recent years. In the improvement of these studies, especially the reduction of estimation errors by downscaling the outputs of global climate models played an important role. In this study, a model that can predict monthly precipitation amounts in the future by using downscaling methods in different global climate models were applied in Antakya district of Hatay province, and the model results were evaluated. The predictive parameters for global climate models were determined using downscaling methods by applying correlation analysis for the study area. As a result of this analysis, it was seen that the air temperature and specific humidity values at the pressure level of 925 hPa and the geopotential height value at the 300 hPa pressure level had the best correlation for the years 1970-2005. The usability of three different global climate models (CanESM2, GISS-E2H, and CSIRO Mk 3-6-0) for the forecast of future rainfall in the Antakya district of Hatay province was investigated using multiple linear regression analysis, one of the downscaling methods. As a result of the statistical analysis, it was seen that the use of the downscaling method increased the accuracy of all prediction models.</p>

scholarly journals Spatial downscaling method and application of CMIP5 global climate models based on improved U-Net in Lake Poyang Basin

2022 ◽  
Vol 34 (1) ◽  
pp. 320-333
Author(s):  
Li Kecheng ◽  
◽  
Lu Jianzhong ◽  
Zhang Kerui ◽  
Lu Chengyu ◽  
...  
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Downscaling Method ◽  
Lake Poyang ◽  
Spatial Downscaling

scholarly journals Effect of changing climate on rice water requirement in Guilan, north of Iran

2016 ◽  
Vol 8 (1) ◽  
pp. 177-190 ◽  
Author(s):  
Hossein Hadinia ◽  
Nader Pirmoradian ◽  
Afshin Ashrafzadeh
Keyword(s):  
Climate Models ◽  
Reference Evapotranspiration ◽  
Global Climate ◽  
Baseline Period ◽  
Water Requirement ◽  
Global Climate Models ◽  
Weather Data ◽  
Stochastic Weather Generator ◽  
North Of Iran ◽  
Rice Water

In this study, the effectiveness of 15 global climate models (GCMs) for simulating weather data of Rasht synoptic station in the north of Iran was evaluated using a statistical downscaling approach. Downscaling of GCMs was performed using a stochastic weather generator model (LARS-WG5.5) and the best GCM (INCM3) was selected. The parameters such as precipitation, radiation, temperature and reference evapotranspiration were simulated using the selected GCMs for two periods of 2013–2042 and 2043–2072, and accordingly, the rice water requirement was estimated for the coming periods. Then, simulated results were compared with data in the baseline period (1981–2010). The results showed that reference evapotranspiration (ETo) for all the seasons will increase in the coming periods. The highest ETo increase (18.5–23.7 mm month–1) will occur in the spring. Also, the average rice water requirement will increase between 178 and 572 m3 ha–1 depending on the emission scenarios and future studied periods. The incremental changes in ETo and, consequently, in rice water requirement for the coming periods will occur as a result of the significant increase in temperature. The results of this study can be used by local planners as a correct view of water demand in the future.

Extreme Events in High Resolution CMCC Regional and Global Climate Models

2011 ◽  
Author(s):  
Enrico Scoccimarro ◽  
Silvio Gualdi ◽  
Antonella Sanna ◽  
Edoardo Bucchignani ◽  
Myriam Montesarchio
Keyword(s):  
High Resolution ◽  
Extreme Events ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models

scholarly journals Global climatology and trends in convective environments from ERA5 and rawinsonde data

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Mateusz Taszarek ◽  
John T. Allen ◽  
Mattia Marchio ◽  
Harold E. Brooks
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Convective Available Potential Energy ◽  
Global Climate Models ◽  
Convective Precipitation ◽  
The Past ◽  
The Tropics ◽  
Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

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