scholarly journals Contributions of weakly coupled data assimilation-based land initialization to interannual predictability of summer climate over Europe

2021 ◽  
pp. 1-55
Author(s):  
Pengfei Shi ◽  
Bin Wang ◽  
Yujun He ◽  
Hui Lu ◽  
Kun Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Potential Predictability ◽  
Climate System Model ◽  
Climate Predictability ◽  
Summer Climate ◽  
Weakly Coupled ◽  
Coupled Data Assimilation

AbstractLand surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.

Coupled data assimilation in climate research: A brief review of applications in ocean and land

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Kazuyoshi Suzuki 1 ◽  
Milija ZUPANSKI 2
Keyword(s):  
Data Assimilation ◽  
Observational Data ◽  
Initial Conditions ◽  
Monitoring Networks ◽  
Coupled Models ◽  
Climate Change Research ◽  
Empirical Parameter ◽  
Strongly Coupled ◽  
Weakly Coupled ◽  
Coupled Data Assimilation

Regions of the cryosphere, including the poles, that are currently unmonitored are expanding, therefore increasing the importance of satellite observations for such regions. With the increasing availability of satellite data in recent years, data assimilation research that combines forecasting models with observational data has begun to flourish. Coupled land/ice-atmosphere/ocean models generally improve the forecasting ability of models. Data assimilation plays an important role in such coupled models, by providing initial conditions and/or empirical parameter estimation. Coupled data assimilation can generally be divided into three types: uncoupled, weakly coupled, or strongly coupled. This review provides an overview of coupled data assimilation, introduces examples of its use in research on sea ice-ocean interactions and the land, and discusses its future outlook. Assimilation of coupled data constitutes an effective method for monitoring cold regions for which observational data are scarce and should prove useful for climate change research and the design of efficient monitoring networks in the future.

The impact of coupled data assimilation on Madden-Julian Oscillation predictability initialized from coupled satellite-era reanalysis

2021 ◽  
Author(s):  
Junchen Yao ◽  
Frédéric Vitart ◽  
Magdalena Alonso Balmaseda ◽  
Tongwen Wu ◽  
Xiangwen Liu
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Wave Radiation ◽  
Madden Julian Oscillation ◽  
Budget Analysis ◽  
Downward Longwave Radiation ◽  
Extended Range ◽  
Coupled Data Assimilation ◽  
The Impact

AbstractThis study investigates the impact of coupled initialization on the extended-range prediction of the Madden-Julian Oscillation (MJO). A set of reforecasts using combinations of the oceanic and atmospheric initial conditions produced with coupled and uncoupled data assimilation (DA) are conducted to evaluate the impact of coupling in the different domains, from the perspective of MJO forecasts. The coupled initial conditions are provided by CERA-SAT pilot coupled reanalysis for the satellite era recently produced by ECMWF. We focus on the prediction skill of the MJO using the Real-time Outgoing Long-wave Radiation (OLR) MJO index in a series of re-forecasts. The impact of atmospheric initial conditions produced by coupled DA shows slight benefit for the MJO prediction. However, compared with the operational ocean reanalysis, the ocean initial conditions created by CERA-SAT degrade the MJO prediction skill during the first 2-3 weeks of the re-forecast by 1.5% to 5.8%. A moist static energy budget analysis revealed that the underestimation of 0.2 K sea surface temperature, 1.4 W m-2 top of atmosphere downward longwave radiation, and 3.8 W m-2 latent heat flux over the Maritime Continent lead to small but statistically significant degradation of the MJO forecast skill. The results demonstrate that the MJO is sensitive to ocean initial conditions, and illustrate the value of the extended range MJO prediction for evaluating the quality of coupled data assimilation, and suggest that future efforts on coupled data assimilation pay special attention to the balance of air-sea interaction processes over the warm pool area, in terms of modeling, observational needs and system.

scholarly journals PNA Predictability at Various Time Scales

2013 ◽  
Vol 26 (22) ◽  
pp. 9090-9114 ◽  
Author(s):  
Waqar Younas ◽  
Youmin Tang
Keyword(s):  
Time Scales ◽  
Initial Conditions ◽  
Prediction Skill ◽  
Multiple Model ◽  
Coupled Models ◽  
Potential Predictability ◽  
Effective Measure ◽  
Dispersion Component ◽  
Significant Difference ◽  
Better Than

Abstract In this study, the predictability of the Pacific–North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple-model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) ensemble, and the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) multiple-model ensemble (MME) skill was better than most of the individual models; (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months; (iii) there is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models; (iv) relative entropy (REA) is an effective measure in characterizing the potential predictability of individual prediction, whereas the mutual information (MI) is a reliable indicator of overall prediction skill; and (v) compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. Further analysis found that the signal component dominated the dispersion component in REA for PNA potential predictability from days to seasons. Also, the PNA predictability is highly related to the signal of the tropical sea surface temperature (SST), and SST–PNA correlation patterns resemble the typical ENSO structure, suggesting that ENSO is the main source of PNA seasonal predictability. The predictable component analysis (PrCA) of atmospheric variability further confirmed the above conclusion; that is, PNA is one of the most predictable patterns in the climate variability over the Northern Hemisphere, which originates mainly from the ENSO forcing.

scholarly journals Assessment of ocean analysis and forecast from an atmosphere–ocean coupled data assimilation operational system

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1307-1326 ◽  
Author(s):  
Catherine Guiavarc'h ◽  
Jonah Roberts-Jones ◽  
Chris Harris ◽  
Daniel J. Lea ◽  
Andrew Ryan ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Current System ◽  
Weather Prediction ◽  
Surface Ocean ◽  
Weakly Coupled ◽  
Ocean Prediction ◽  
Coupled Data Assimilation ◽  
Ocean Forecasting ◽  
Prediction Systems

Abstract. The development of coupled atmosphere–ocean prediction systems with utility on short-range numerical weather prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between the surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean–atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a “coupled NWP” system at the Met Office.

scholarly journals Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation system

2019 ◽  
Author(s):  
Milija Zupanski ◽  
Anton Kliewer ◽  
Ting-Chi Wu ◽  
Karina Apodaca ◽  
Qijing Bian ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Initial Conditions ◽  
Positive Impact ◽  
Storm Event ◽  
Strongly Coupled ◽  
Atmospheric Observations ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.

Examining the Impact of SMAP Soil Moisture Retrievals on Short-Range Weather Prediction under Weakly and Strongly Coupled Data Assimilation with WRF-Noah

2019 ◽  
Vol 147 (12) ◽  
pp. 4345-4366 ◽  
Author(s):  
Liao-Fan Lin ◽  
Zhaoxia Pu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Weather Prediction ◽  
Surface Model ◽  
Strongly Coupled ◽  
Soil Moisture Data ◽  
Analysis Scheme ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract Remotely sensed soil moisture data are typically incorporated into numerical weather models under a framework of weakly coupled data assimilation (WCDA), with a land surface analysis scheme independent from the atmospheric analysis component. In contrast, strongly coupled data assimilation (SCDA) allows simultaneous correction of atmospheric and land surface states but has not been sufficiently explored with land surface soil moisture data assimilation. This study implemented a variational approach to assimilate the Soil Moisture Active Passive (SMAP) 9-km enhanced retrievals into the Noah land surface model coupled with the Weather Research and Forecasting (WRF) Model under a framework of both WCDA and SCDA. The goal of the study is to quantify the relative impact of assimilating SMAP data under different coupling frameworks on the atmospheric forecasts in the summer. The results of the numerical experiments during July 2016 show that SCDA can provide additional benefits on the forecasts of air temperature and humidity compared to WCDA. Over the U.S. Great Plains, assimilation of SMAP data under WCDA reduces a warm bias in temperature and a dry bias in humidity by 7.3% and 19.3%, respectively, while the SCDA case contributes an additional bias reduction of 2.2% (temperature) and 3.3% (humidity). While WCDA leads to a reduction of RMSE in temperature forecasts by 4.1%, SCDA results in additional reduction of RMSE by 0.8%. For the humidity, the reduction of RMSE is around 1% for both WCDA and SCDA.

Air-sea strongly coupled data assimilation for tropical cyclone prediction

2021 ◽  
Author(s):  
Xingchao Chen
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Forecast Error ◽  
Regional Scale ◽  
Forecast Errors ◽  
State Variables ◽  
Strongly Coupled ◽  
Weakly Coupled ◽  
Coupled Data Assimilation ◽  
Tc Prediction

<p>Air-sea interactions are critical to tropical cyclone (TC) energetics. However, oceanic state variables are still poorly initialized, and are inconsistent with atmospheric initial fields in most operational coupled TC forecast models. In this study, we first investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018) using a 200-member ensemble of convection-permitting forecasts from a coupled atmosphere-ocean regional model. Meaningful and dynamically consistent cross domain ensemble error correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update model state variables associated with the coupled ocean-atmosphere prediction of TCs using strongly coupled data assimilation (DA). A regional-scale strongly coupled DA system based on the ensemble Kalman filter (EnKF) is then developed for TC prediction. The potential impacts of different atmospheric and oceanic observations on TC analysis and prediction are examined through observing system simulation experiments (OSSEs) of Hurricane Florence (2018). Results show that strongly coupled DA resulted in better analysis and forecast of both the oceanic and atmospheric variables than weakly coupled DA. Compared to weakly coupled DA in which the analysis update is performed separately for the atmospheric and oceanic domains, strongly coupled DA reduces the forecast errors of TC track and intensity. Results show promise in potential further improvement in TC prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based strongly coupled DA system.</p>

scholarly journals Evaluation of a new middle-lower tropospheric CO<sub>2</sub> product using data assimilation

2013 ◽  
Vol 13 (9) ◽  
pp. 4487-4500 ◽  
Author(s):  
A. Tangborn ◽  
L. L. Strow ◽  
B. Imbiriba ◽  
L. Ott ◽  
S. Pawson
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Model Simulation ◽  
Lower Troposphere ◽  
Earth Observing System ◽  
Peak Sensitivity ◽  
The Difference ◽  
Using Data ◽  
Mean Differences

Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the GEOS-5 (Goddard Earth Observing System Model, Version 5) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the used channels contain useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in situ surface observation locations in more remote areas.

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