On Space Weather Data Assimilation

2020 ◽  
Author(s):  
Mihail Codrescu ◽  
Stefan Codrescu ◽  
Mariangel Fedrizzi ◽  
Claudia Borries
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Prediction Models ◽  
Mass Density ◽  
Weather Prediction ◽  
Weather Data ◽  
Space Systems ◽  
Use Of Data ◽  
Data Assimilation System ◽  
Numerical Weather Prediction Models

<p>Most if not all terrestrial weather prediction services today are based on data assimilation and numerical weather prediction models. Space Weather services are expected to follow a similar path towards data assimilation. However, the application of data assimilation in Space Weather requires a different implementation compared to terrestrial weather because space systems tend to be strongly forced and because the amount of data available for assimilation is critically small. In this paper we review the implementation of an ensemble Kalman filter data assimilation system based on the Space Weather Prediction Center operational Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model. We present assimilation results for neutral mass density during geomagnetically quiet and disturbed conditions and discuss the future use of data assimilation for the thermosphere ionosphere system.<span> </span></p>

scholarly journals Ensemble–Variational Integrated Localized Data Assimilation

2016 ◽  
Vol 144 (10) ◽  
pp. 3677-3696 ◽  
Author(s):  
Thomas Auligné ◽  
Benjamin Ménétrier ◽  
Andrew C. Lorenc ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Deterministic System ◽  
Current State ◽  
Filter Methods ◽  
Variational Framework ◽  
Operational Systems ◽  
Numerical Weather Prediction Models ◽  
New Framework

Hybrid variational–ensemble data assimilation (hybrid DA) is widely used in research and operational systems, and it is considered the current state of the art for the initialization of numerical weather prediction models. However, hybrid DA requires a separate ensemble DA to estimate the uncertainty in the deterministic variational DA, which can be suboptimal both technically and scientifically. A new framework called the ensemble–variational integrated localized (EVIL) data assimilation addresses this inconvenience by updating the ensemble analyses using information from the variational deterministic system. The goal of EVIL is to encompass and generalize existing ensemble Kalman filter methods in a variational framework. Particular attention is devoted to the affordability and efficiency of the algorithm in preparation for operational applications.

Assimilation of Precipitation-Affected Radiances in a Cloud-Resolving WRF Ensemble Data Assimilation System

2013 ◽  
Vol 141 (2) ◽  
pp. 754-772 ◽  
Author(s):  
Sara Q. Zhang ◽  
Milija Zupanski ◽  
Arthur Y. Hou ◽  
Xin Lin ◽  
Samson H. Cheung
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Rain Event ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Cross Covariance ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto- and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.

Limited-Area Ensemble-Based Data Assimilation

2011 ◽  
Vol 139 (7) ◽  
pp. 2025-2045 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Data Assimilation ◽  
Variational Methods ◽  
Prediction Models ◽  
Weather Prediction ◽  
Limited Area ◽  
Ensemble Kalman Filters ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Error Covariance ◽  
Numerical Weather Prediction Models

Abstract Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, this article reviews recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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