Improved depiction of Indian summer monsoon in latest high resolution NCEP climate forecast system reanalysis

2014 ◽  
Vol 35 (10) ◽  
pp. 3102-3119 ◽  
Author(s):  
Hemantkumar S. Chaudhari ◽  
Samir Pokhrel ◽  
Subodh K. Saha ◽  
Ashish Dhakate ◽  
Anupam Hazra
Keyword(s):  
High Resolution ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Ncep Climate Forecast System

Improved simulation of Indian summer monsoon in latest NCEP climate forecast system free run

2013 ◽  
Vol 34 (5) ◽  
pp. 1628-1641 ◽  
Author(s):  
Subodh K. Saha ◽  
Samir Pokhrel ◽  
Hemantkumar S. Chaudhari ◽  
Ashish Dhakate ◽  
Swati Shewale ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

Improvements in the representation of the Indian summer monsoon in the NCEP climate forecast system version 2

2015 ◽  
Vol 45 (9-10) ◽  
pp. 2485-2498 ◽  
Author(s):  
Rodrigo J. Bombardi ◽  
Edwin K. Schneider ◽  
Lawrence Marx ◽  
Subhadeep Halder ◽  
Bohar Singh ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

ENSO, IOD and Indian Summer Monsoon in NCEP climate forecast system

2012 ◽  
Vol 39 (9-10) ◽  
pp. 2143-2165 ◽  
Author(s):  
Samir Pokhrel ◽  
H. S. Chaudhari ◽  
Subodh K. Saha ◽  
Ashish Dhakate ◽  
R. K. Yadav ◽  
...  
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

scholarly journals Role of the Indian Ocean in the ENSO–Indian Summer Monsoon Teleconnection in the NCEP Climate Forecast System

2012 ◽  
Vol 25 (7) ◽  
pp. 2490-2508 ◽  
Author(s):  
Deepthi Achuthavarier ◽  
V. Krishnamurthy ◽  
Ben P. Kirtman ◽  
Bohua Huang
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Surface Pressure ◽  
Indian Summer Monsoon ◽  
Coupled Model ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
The Indian Ocean ◽  
Ncep Climate Forecast System

Abstract The observed negative correlation between El Niño–Southern Oscillation (ENSO) and the Indian summer monsoon is not simulated by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) coupled model. The correlation is partially restored in the simulations where the Indian Ocean (IO) sea surface temperature (SST) is prescribed with the daily mean or climatology. Comparison among the simulations suggests that ENSO-induced SST anomalies form a strong dipole pattern oriented along the zonal direction in the IO in the coupled model, preventing the ENSO signals from reaching the Indian monsoon region. In the model, the dipole develops early in the monsoon season and extends to the central equatorial IO while it is formed at the end of the season in observations. The dipole modifies low-level winds and surface pressure, and grows in a positive feedback loop involving winds, surface pressure, and SST. Examination of the mean state in the model reveals that the thermocline is relatively shallow in the eastern IO. This preconditions the ocean such that the atmospheric fluxes can easily impart fluctuations in the subsurface temperature and thereby in the SST. These results suggest that biases in the IO can adversely affect the ENSO–monsoon teleconnection in a coupled model.

Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

2013 ◽  
Vol 42 (7-8) ◽  
pp. 1925-1947 ◽  
Author(s):  
J. S. Chowdary ◽  
H. S. Chaudhari ◽  
C. Gnanaseelan ◽  
Anant Parekh ◽  
A. Suryachandra Rao ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Summer Monsoon ◽  
Tropical Indian Ocean ◽  
Monsoon Circulation ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Ncep Climate Forecast System

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.

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