Observing System Simulation Experiments double scientific return of surface-atmosphere synthesis

2021 ◽  
Author(s):  
Stefan Metzger ◽  
David Durden ◽  
Sreenath Paleri ◽  
Matthias Sühring ◽  
Brian J. Butterworth ◽  
...  
Keyword(s):  
System Simulation ◽  
Simulation Experiments ◽  
Surface Atmosphere ◽  
Scientific Return

Observing System Simulation Experiments double scientific return of surface-atmosphere synthesis

2021 ◽  
Author(s):  
Stefan Metzger ◽  
David Durden ◽  
Sreenath Paleri ◽  
Matthias Sühring ◽  
Brian J. Butterworth ◽  
...  
Keyword(s):  
System Simulation ◽  
Simulation Experiments ◽  
Surface Atmosphere ◽  
Scientific Return

scholarly journals Observing system simulation experiments double scientific return of surface-atmosphere synthesis

2021 ◽  
Author(s):  
Stefan Metzger ◽  
David Durden ◽  
Sreenath Paleri ◽  
Matthias Sühring ◽  
Brian Butterworth ◽  
...  
Keyword(s):  
Energy Balance ◽  
System Simulation ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Environmental Response ◽  
Simulation Experiments ◽  
Balance Study ◽  
Scientific Success ◽  
Surface Atmosphere ◽  
Novel Approach

Abstract. The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19).During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals.We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.

scholarly journals Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements

2021 ◽  
Vol 14 (11) ◽  
pp. 6929-6954
Author(s):  
Stefan Metzger ◽  
David Durden ◽  
Sreenath Paleri ◽  
Matthias Sühring ◽  
Brian J. Butterworth ◽  
...  
Keyword(s):  
Energy Balance ◽  
Information Gain ◽  
System Simulation ◽  
Field Measurements ◽  
Environmental Response ◽  
Simulation Experiments ◽  
Balance Study ◽  
Scientific Success ◽  
Surface Atmosphere ◽  
System Designs

Abstract. The observing system design of multidisciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increases in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel numerical simulation–environmental response function (NS–ERF) approach to observing system simulation experiments that aids surface–atmosphere synthesis at the interface of mesoscale and microscale meteorology. In a case study we demonstrate application of the NS–ERF approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered the placement of 20 eddy covariance flux towers, operations for 72 h of low-altitude flux aircraft measurements, and integration of various remote sensing data products. A 2 h high-resolution large eddy simulation created a cloud-free virtual atmosphere for surface and meteorological conditions characteristic of the field campaign domain and period. To explore two specific design hypotheses we super-sampled this virtual atmosphere as observed by 13 different yet simultaneous observing system designs consisting of virtual ground, airborne, and satellite observations. We then analyzed these virtual observations through ERFs to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19's potential to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its modular extensibility, NS–ERF lends itself to optimizing observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection, and multi-species applications, among other use cases.

scholarly journals Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments

2018 ◽  
Vol 146 (1) ◽  
pp. 175-198 ◽  
Author(s):  
Rong Kong ◽  
Ming Xue ◽  
Chengsi Liu
Keyword(s):  
Data Assimilation ◽  
System Simulation ◽  
Radar Data ◽  
Mixing Ratio ◽  
State Variables ◽  
Ensemble Size ◽  
Simulation Experiments ◽  
Background Error ◽  
Variational Minimization ◽  
Water Vapor Mixing Ratio

Abstract A hybrid ensemble–3DVar (En3DVar) system is developed and compared with 3DVar, EnKF, “deterministic forecast” EnKF (DfEnKF), and pure En3DVar for assimilating radar data through perfect-model observing system simulation experiments (OSSEs). DfEnKF uses a deterministic forecast as the background and is therefore parallel to pure En3DVar. Different results are found between DfEnKF and pure En3DVar: 1) the serial versus global nature and 2) the variational minimization versus direct filter updating nature of the two algorithms are identified as the main causes for the differences. For 3DVar (EnKF/DfEnKF and En3DVar), optimal decorrelation scales (localization radii) for static (ensemble) background error covariances are obtained and used in hybrid En3DVar. The sensitivity of hybrid En3DVar to covariance weights and ensemble size is examined. On average, when ensemble size is 20 or larger, a 5%–10% static covariance gives the best results, while for smaller ensembles, more static covariance is beneficial. Using an ensemble size of 40, EnKF and DfEnKF perform similarly, and both are better than pure and hybrid En3DVar overall. Using 5% static error covariance, hybrid En3DVar outperforms pure En3DVar for most state variables but underperforms for hydrometeor variables, and the improvement (degradation) is most notable for water vapor mixing ratio qυ (snow mixing ratio qs). Overall, EnKF/DfEnKF performs the best, 3DVar performs the worst, and static covariance only helps slightly via hybrid En3DVar.

Observing System Simulation Experiments to Assess the Potential Impact of New Observing Systems on Hurricane Forecasting

2015 ◽  
Vol 49 (6) ◽  
pp. 140-148 ◽  
Author(s):  
Robert Atlas ◽  
Lisa Bucci ◽  
Bachir Annane ◽  
Ross Hoffman ◽  
Shirley Murillo
Keyword(s):  
System Simulation ◽  
Weather Prediction ◽  
Simulation Experiments ◽  
Nasa Goddard Space ◽  
Trade Offs ◽  
Goddard Space Flight Center ◽  
Hurricane Track ◽  
Potential Impact ◽  
Hurricane Forecasting ◽  
Observing Systems

AbstractObserving System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of new or proposed observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at the National Aeronautical and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) Atlantic Oceanographic and Meteorological Laboratory (AOML) over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch; evaluated trade-offs in orbits, coverage, and accuracy for space-based wind lidars; and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. This paper summarizes early applications of global OSSEs to hurricane track forecasting and new experiments using both global and regional models. These latter experiments are aimed at assessing potential impact on hurricane track and intensity prediction over the oceans and at landfall.

scholarly journals The Impact of Covariance Localization for Radar Data on EnKF Analyses of a Developing MCS: Observing System Simulation Experiments

2013 ◽  
Vol 141 (11) ◽  
pp. 3691-3709 ◽  
Author(s):  
Ryan A. Sobash ◽  
David J. Stensrud
Keyword(s):  
System Simulation ◽  
Three Dimensional ◽  
Radar Data ◽  
Convective System ◽  
State Variables ◽  
Simulation Experiments ◽  
Length Scales ◽  
Convective Scale ◽  
The Impact ◽  
Prior State

Abstract Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from a truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18 km in the horizontal and 3, 6, and 12 km in the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallest RMSE for most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal localization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that were similar to the horizontal and vertical length scales of the prior state correlations, especially for observation-state correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF experiments that assimilate radar data are discussed.

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