scholarly journals PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE

2020 ◽  
Vol 4 (3) ◽  
pp. 473-483
Author(s):  
Riza Indriani Rakhmalia ◽  
Agus M Soleh ◽  
Bagus Sartono
Keyword(s):  
Statistical Downscaling ◽  
Daily Rainfall ◽  
Error Prediction ◽  
Mean Square ◽  
Forecast System ◽  
Climate Forecast System ◽  
Data Set ◽  
Climate Forecast System Reanalysis ◽  
Rainfall Prediction ◽  
Tweedie Distribution

Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.

Climatic characteristics of drift ice in the northern Barents Sea

2019 ◽  
Vol 11 (3) ◽  
pp. 800-811
Author(s):  
Chenglin Duan ◽  
Sheng Dong ◽  
Zhifeng Wang ◽  
Zhenkun Liao
Keyword(s):  
Barents Sea ◽  
Ice Thickness ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Ice Velocity ◽  
Environmental Prediction ◽  
Climatic Characteristics

Abstract In this paper, a preliminary climatic description of the long-term offshore drift ice characteristics in the northern Barents Sea has been investigated from 1987 to 2016 based on the satellite ice motion datasets from National Snow and Ice Data Center (NSIDC) and reanalysis ice thickness datasets from National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR) and Climate Forecast System Version 2 (CFSv2). Both the ice velocity and thickness conditions have been studied at the three fixed locations from west to east. Annual and monthly drift ice roses indicate that the directions from WSW to SE are primarily prevailing, particularly in winter months. Besides, the annual ice speed extremums exceeding 40 cm s–1 mostly occur in the southerly directions from November to April. For the ice thickness, results reveal that it is prominently distributed in a thicker interval between 70 and 120 cm, and a thinner interval between 20 and 70 cm. The annual thickness maxima approximately range from 90 to 170 cm, primarily occurring from May to June, and demonstrate a light decreasing trend.

scholarly journals Evaluation of the Climate Forecast System Reanalysis weather data for the hydrological model in the Arctic watershed Målselv

2021 ◽  
Author(s):  
Minh Tuan Bui ◽  
Jinmei Lu ◽  
Linmei Nie
Keyword(s):  
High Resolution ◽  
Hydrological Model ◽  
The Arctic ◽  
Annual Rainfall ◽  
Climate Forecast ◽  
Hydrological Models ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were conducted by the model for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrate its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data are a reliable source for running hydrological models in the Arctic watershed Målselv.

scholarly journals The Land Surface Analysis in the NCEP Climate Forecast System Reanalysis

2012 ◽  
Vol 13 (5) ◽  
pp. 1621-1630 ◽  
Author(s):  
Jesse Meng ◽  
Rongqian Yang ◽  
Helin Wei ◽  
Michael Ek ◽  
George Gayno ◽  
...  
Keyword(s):  
Surface Energy ◽  
Surface Analysis ◽  
Land Surface ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Data Assimilation System ◽  
Ncep Climate Forecast System ◽  
Assimilation System

Abstract The NCEP Climate Forecast System Reanalysis (CFSR) uses the NASA Land Information System (LIS) to create its land surface analysis: the NCEP Global Land Data Assimilation System (GLDAS). Comparing to the previous two generations of NCEP global reanalyses, this is the first time a coupled land–atmosphere data assimilation system is included in a global reanalysis. Global observed precipitation is used as direct forcing to drive the land surface analysis, rather than the typical reanalysis approach of using precipitation assimilating from a background atmospheric model simulation. Global observed snow cover and snow depth fields are used to constrain the simulated snow variables. This paper describes 1) the design and implementation of GLDAS/LIS in CFSR, 2) the forcing of the observed global precipitation and snow fields, and 3) preliminary results of global and regional soil moisture content and land surface energy and water budgets closure. With special attention made during the design of CFSR GLDAS/LIS, all the source and sink terms in the CFSR land surface energy and water budgets can be assessed and the total budgets are balanced. This is one of many aspects indicating improvements in CFSR from the previous NCEP reanalyses.

Patterns and cycles in the Climate Forecast System Reanalysis wind and wave data

2013 ◽  
Vol 70 ◽  
pp. 207-220 ◽  
Author(s):  
Justin E. Stopa ◽  
Kwok Fai Cheung ◽  
Hendrik L. Tolman ◽  
Arun Chawla
Keyword(s):  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Wave Data

scholarly journals Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

2015 ◽  
Vol 28 (3) ◽  
pp. 1166-1183 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman
Keyword(s):  
United States ◽  
Spatial Variability ◽  
El Niño ◽  
El Nino ◽  
The United States ◽  
Total Variance ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

A Comparison of Extratropical Cyclones in Recent Reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25

2011 ◽  
Vol 24 (18) ◽  
pp. 4888-4906 ◽  
Author(s):  
K. I. Hodges ◽  
R. W. Lee ◽  
L. Bengtsson
Keyword(s):  
The Other ◽  
Extratropical Cyclones ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Spatial Differences ◽  
The Mean ◽  
Using Data ◽  
Modern Era ◽  
Ncep Climate Forecast System

Abstract Extratropical cyclones are identified and compared using data from four recent reanalyses for the winter periods in both hemispheres. Results show the largest differences occur between the older lower resolution 25-yr Japanese Reanalysis (JRA-25) when compared with the newer high resolution reanalyses, particularly in the Southern Hemisphere (SH). Spatial differences between the newest reanalyses are small in both hemispheres and generally not significant except in some common regions associated with cyclogenesis close to orography. Differences in the cyclone maximum intensitites are generally related to spatial resolution except in the NASA Modern Era Retrospective-Analysis for Research and Applications (NASA MERRA), which has larger intensities for several different measures. Matching storms between reanalyses shows the number matched between the ECMWF Interim Re-Analysis (ERA-Interim) and the other reanalyses is similar in the Northern Hemisphere (NH). In the SH the number matched between JRA-25 and ERA-Interim is lower than in the NH; however, for NASA MERRA and the NCEP Climate Forecast System Reanalysis (NCEP CFSR), the number matched is similar to the NH. The mean separation of the identically same cyclones is typically less than 2° geodesic in both hemispheres for the latest reanalyses, whereas JRA-25 compared with the other reanalyses has a broader distribution in the SH, indicating greater uncertainty. The instantaneous intensity differences for matched storms shows narrow distributions for pressure, while for winds and vorticity the distributions are much broader, indicating larger uncertainty typical of smaller-scale fields. Composite cyclone diagnostics show that cyclones are very similar between the reanalyses, with differences being related to the intensities, consistent with the intensity results. Overall, results show NH cyclones correspond well between reanalyses, with a significant improvement in the SH for the latest reanalyses, indicating a convergence between reanalyses for cyclone properties.

Sign in / Sign up

Export Citation Format

Share Document