scholarly journals Development of the Korean Peninsula-Korean Aviation Turbulence Guidance (KP-KTG) System Using the Local Data Assimilation and Prediction System (LDAPS) of the Korea Meteorological Administration (KMA)

Atmosphere ◽  
2015 ◽  
Vol 25 (2) ◽  
pp. 367-374 ◽  
Author(s):  
Dan-Bi Lee ◽  
Hye-Yeong Chun
Keyword(s):  
Data Assimilation ◽  
Korean Peninsula ◽  
Prediction System ◽  
Local Data ◽  
Korea Meteorological Administration

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

Local data assimilation and analysis for nowcasting

1992 ◽  
Vol 12 (7) ◽  
pp. 179-188 ◽  
Author(s):  
John A. McGinley ◽  
Steven C. Albers ◽  
Peter A. Stamus
Keyword(s):  
Data Assimilation ◽  
Local Data

scholarly journals Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0

2017 ◽  
Vol 10 (3) ◽  
pp. 1107-1129 ◽  
Author(s):  
Enza Di Tomaso ◽  
Nick A. J. Schutgens ◽  
Oriol Jorba ◽  
Carlos Pérez García-Pando
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Mineral Dust ◽  
Positive Impact ◽  
Forecast Error ◽  
Weather Prediction ◽  
Prediction System ◽  
Deep Blue ◽  
Modis Aod ◽  
Sahel Region

Abstract. A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.

The impact of Argo observations in a global weakly coupled ocean–atmosphere data assimilation and short‐range prediction system

2019 ◽  
Vol 146 (726) ◽  
pp. 401-414 ◽  
Author(s):  
Robert R. King ◽  
Daniel J. Lea ◽  
Matthew J. Martin ◽  
Isabelle Mirouze ◽  
Julian Heming
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
Prediction System ◽  
Weakly Coupled ◽  
Ocean Atmosphere ◽  
The Impact

scholarly journals Evaluation of Rainfall Forecasts with Heavy Rain Types in the High-Resolution Unified Model over South Korea

2019 ◽  
Vol 34 (5) ◽  
pp. 1277-1293 ◽  
Author(s):  
Hwan-Jin Song ◽  
Byunghwan Lim ◽  
Sangwon Joo
Keyword(s):  
High Resolution ◽  
South Korea ◽  
Korean Peninsula ◽  
Heavy Rainfall ◽  
Rainfall Intensity ◽  
Heavy Rain ◽  
Unified Model ◽  
Moderate Intensity ◽  
Local Data ◽  
Total Rainfall

Abstract Heavy rainfall events account for most socioeconomic damages caused by natural disasters in South Korea. However, the microphysical understanding of heavy rain is still lacking, leading to uncertainties in quantitative rainfall prediction. This study is aimed at evaluating rainfall forecasts in the Local Data Assimilation and Prediction System (LDAPS), a high-resolution configuration of the Unified Model over the Korean Peninsula. The rainfall of LDAPS forecasts was evaluated with observations based on two types of heavy rain events classified from K-means clustering for the relationship between surface rainfall intensity and cloud-top height. LDAPS forecasts were characterized by more heavy rain cases with high cloud-top heights (cold-type heavy rain) in contrast to observations showing frequent moderate-intensity rain systems with relatively lower cloud-top heights (warm-type heavy rain) over South Korea. The observed cold-type and warm-type events accounted for 32.7% and 67.3% of total rainfall, whereas LDAPS forecasts accounted for 65.3% and 34.7%, respectively. This indicates severe overestimation and underestimation of total rainfall for the cold-type and warm-type forecast events, respectively. The overestimation of cold-type heavy rainfall was mainly due to its frequent occurrence, whereas the underestimation of warm-type heavy rainfall was affected by both its low occurrence and weak intensity. The rainfall forecast skill for the warm-type events was much lower than for the cold-type events, due to the lower rainfall intensity and smaller rain area of the warm-type. Therefore, cloud parameterizations for warm-type heavy rain should be improved to enhance rainfall forecasts over the Korean Peninsula.

scholarly journals Ocean Data Assimilation and Initialization Procedure for the Coupled GFDL/URI Hurricane Prediction System

2005 ◽  
Vol 22 (12) ◽  
pp. 1918-1932 ◽  
Author(s):  
Aleksandr Falkovich ◽  
Isaac Ginis ◽  
Stephen Lord
Keyword(s):  
Data Assimilation ◽  
Rhode Island ◽  
Field Experiments ◽  
Feature Modeling ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Prediction System ◽  
Modeling Approach ◽  
Ocean Data Assimilation ◽  
Hurricane Prediction ◽  
Initialization Procedure

Abstract A new ocean data assimilation and initialization procedure is presented. It was developed to obtain more realistic initial ocean conditions, including the position and structure of the Gulf Stream (GS) and Loop Current (LC), in the Geophysical Fluid Dynamics Laboratory/University of Rhode Island (GFDL/URI) coupled hurricane prediction system used operationally at the National Centers for Environmental Prediction. This procedure is based on a feature-modeling approach that allows a realistic simulation of the cross-frontal temperature, salinity, and velocity of oceanic fronts. While previous feature models used analytical formulas to represent frontal structures, the new procedure uses the innovative method of cross-frontal “sharpening” of the background temperature and salinity fields. The sharpening is guided by observed cross sections obtained in specialized field experiments in the GS. The ocean currents are spun up by integrating the ocean model for 2 days, which was sufficient for the velocity fields to adjust to the strong gradients of temperature and salinity in the main thermocline in the GS and LC. A new feature-modeling approach was also developed for the initialization of a multicurrent system in the Caribbean Sea, which provides the LC source. The initialization procedure is demonstrated for coupled model forecasts of Hurricane Isidore (2002).

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