Impact of TROPICS Radiances on Tropical Cyclone Prediction in an OSSE

2021 ◽  
Author(s):  
Hui W. Christophersen ◽  
Brittany A. Dahl ◽  
Jason P. Dunion ◽  
Robert F. Rogers ◽  
Frank D. Marks ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Microwave Radiometer ◽  
Atmospheric Temperature ◽  
Tropospheric Temperature ◽  
State Variables ◽  
Time Resolved ◽  
Thermodynamic Structure ◽  
The Tropics ◽  
Observing System Simulation Experiment ◽  
The Impact

AbstractAs part of the NASA Earth Venture-Instrument program, the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, to be launched in January 2022, will deliver unprecedented rapid-update microwave measurements over the tropics that can be used to observe the evolution of the precipitation and thermodynamic structure of tropical cyclones (TCs) at meso- and synoptic scales. TROPICS consists of six CubeSats, each hosting a passive microwave radiometer that provides radiance observations sensitive to atmospheric temperature, water vapor, precipitation, and precipitation-size ice particles. In this study, the impact of TROPICS all-sky radiances on TC analyses and forecasts is explored through a regional mesoscale observing system simulation experiment (OSSE). The results indicate that the TROPICS all-sky radiances can have positive impacts on TC track and intensity forecasts, particularly when some hydrometeor state variables and other state variables of the data assimilation system that are relevant to cloudy radiance assimilation are updated. The largest impact on the model analyses is seen in the humidity fields, regardless of whether or not there are radiances assimilated from other satellites. TROPICS radiances demonstrate large impact on TC analyses and forecasts when other satellite radiances are absent. The assimilation of the all-sky TROPICS radiances without default radiances leads to a consistent improvement in the low- and mid-tropospheric temperature and wind forecasts throughout the five-day forecasts, but only up to 36 h lead time in the humidity forecasts at all pressure levels. This study illustrates the potential benefits of TROPICS data assimilation for TC forecasts and provides a potentially streamlined pathway for transitioning TROPICS data from research to operations post-launch.

Empirical Localization Functions for Ensemble Kalman Filter Data Assimilation in Regions with and without Precipitation

2015 ◽  
Vol 143 (9) ◽  
pp. 3664-3679 ◽  
Author(s):  
Lili Lei ◽  
Jeffrey L. Anderson ◽  
Glen S. Romine
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Ensemble Kalman Filter ◽  
Simulation Experiment ◽  
State Variables ◽  
Improve Performance ◽  
Spatially Homogeneous ◽  
Observing System Simulation Experiment ◽  
The Impact ◽  
Vertical Localization

Abstract For ensemble-based data assimilation, localization is used to limit the impact of observations on physically distant state variables to reduce spurious error correlations caused by limited ensemble size. Traditionally, the localization value applied is spatially homogeneous. Yet there are potentially larger errors and different covariance length scales in precipitation systems, and that may justify the use of different localization functions for precipitating and nonprecipitating regions. Here this is examined using empirical localization functions (ELFs). Using output from an ensemble observing system simulation experiment (OSSE), ELFs provide estimates of horizontal and vertical localization for different observation types in regions with and without precipitation. For temperature and u- and υ-wind observations, the ELFs for precipitating regions are shown to have smaller horizontal localization scales than for nonprecipitating regions. However, the ELFs for precipitating regions generally have larger vertical localization scales than for nonprecipitating regions. The ELFs are smoothed and then applied in three additional OSSEs. Spatially homogeneous ELFs are found to improve performance relative to a commonly used localization function with compact support. When different ELFs are applied in precipitating and nonprecipitating regions, performance is further improved, but varying ELFs by observation type was not found to be as important. Imbalance in initial states caused by use of different localization functions is diagnosed by the domain-averaged surface pressure tendency. Forecasts from analyses with ELFs have smaller surface pressure tendencies than the standard localization, indicating improved initial balance with ELFs.

scholarly journals Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model

2013 ◽  
Vol 13 (3) ◽  
pp. 583-596 ◽  
Author(s):  
M. Coustau ◽  
S. Ricci ◽  
V. Borrell-Estupina ◽  
C. Bouvier ◽  
O. Thual
Keyword(s):  
Data Assimilation ◽  
Hydrological Model ◽  
Peak Discharge ◽  
State Variables ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Southern France ◽  
Flood Peak ◽  
Event Based ◽  
The Impact

Abstract. Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12% improvement in the flood peak magnitude forecast in 75% of cases. The investigation of the other 25% of cases points out a number of precautions for the appropriate use of this data assimilation procedure.

scholarly journals An Alternative Bias Correction Scheme for CrIS Data Assimilation in a Regional Model

2019 ◽  
Vol 147 (3) ◽  
pp. 809-839 ◽  
Author(s):  
Xin Li ◽  
Xiaolei Zou ◽  
Mingjian Zeng
Keyword(s):  
Data Assimilation ◽  
Bias Correction ◽  
Moving Average ◽  
Atmospheric Temperature ◽  
Lapse Rate ◽  
Specific Humidity ◽  
Convective Precipitation ◽  
Temperature Structure ◽  
The Impact ◽  
Mean Square Errors

Bias correction (BC) is a crucial step for satellite radiance data assimilation (DA). In this study, the traditional airmass BC scheme in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) is investigated for Cross-track Infrared Sounder (CrIS) DA. The ability of the airmass predictors to model CrIS biases is diagnosed. Correlations between CrIS observation-minus-background ( O − B) samples and the two lapse rate–related airmass predictors employed by GSI are found to be very weak, indicating that the bias correction contributed by the airmass BC scheme is small. A modified BC scheme, which directly calculates the moving average of O − B departures from data of the previous 2 weeks with respect to scan position and latitudinal band, is proposed and tested. The impact of the modified BC scheme on CrIS radiance DA is compared with the variational airmass BC scheme. Results from 1-month analysis/forecast experiments show that the modified BC scheme removes nearly all scan-dependent and latitude-dependent biases, while residual biases are still found in some channels when the airmass BC scheme is applied. Smaller predicted root-mean-square errors of temperature and specific humidity and higher equivalent threat scores are obtained by the DA experiment using the modified BC scheme. If O − B samples are replaced by observation-minus-analysis ( O − A) samples for bias estimates in the modified BC scheme, the forecast impacts are reduced but remain positive. A convective precipitation case that occurred on 21 August 2016 is investigated. Using the modified BC scheme, the atmospheric temperature structure and the geopotential height structures near trough/ridge areas are better resolved, resulting in better precipitation forecasts.

scholarly journals Assimilation of Surface-Based Boundary Layer Profiler Observations during a Cool-Season Weather Event Using an Observing System Simulation Experiment. Part I: Analysis Impact

2011 ◽  
Vol 139 (8) ◽  
pp. 2309-2326 ◽  
Author(s):  
Jason A. Otkin ◽  
Daniel C. Hartung ◽  
David D. Turner ◽  
Ralph A. Petersen ◽  
Wayne F. Feltz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Simulation Experiment ◽  
System Simulation ◽  
Microwave Radiometer ◽  
Lower Troposphere ◽  
Moisture Profile ◽  
Wind Profiles ◽  
Observing System Simulation Experiment ◽  
The Impact

AbstractIn this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

scholarly journals Assessing the Impact of LAI Data Assimilation on Simulations of the Soil Water Balance and Maize Development Using MOHID-Land

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1367 ◽  
Author(s):  
Tiago Ramos ◽  
Lucian Simionesei ◽  
Ana Oliveira ◽  
Hanaa Darouich ◽  
Ramiro Neves
Keyword(s):  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Hydrological Modeling ◽  
Soil Water Balance ◽  
Landsat 8 ◽  
State Variables ◽  
Area Index ◽  
Final Model ◽  
The Impact

Hydrological modeling at the catchment scale requires the upscaling of many input parameters for better characterizing landscape heterogeneity, including soil, land use and climate variability. In this sense, remote sensing is often considered as a practical solution. This study aimed to access the impact of assimilation of leaf area index (LAI) data derived from Landsat 8 imagery on MOHID-Land’s simulations of the soil water balance and maize state variables (LAI, canopy height, aboveground dry biomass and yield). Data assimilation impacts on final model results were first assessed by comparing distinct modeling approaches to measured data. Then, the uncertainty related to assimilated LAI values was quantified on final model results using a Monte Carlo method. While LAI assimilation improved MOHID-Land’s estimates of the soil water balance and simulations of crop state variables during early stages, it was never sufficient to overcome the absence of a local calibrated crop dataset. Final model estimates further showed great uncertainty for LAI assimilated values during earlier crop stages, decreasing then with season reaching its end. Thus, while model simulations can be improved using LAI data assimilation, additional data sources should be considered for complementing crop parameterization.

scholarly journals Declining Aerosols in CMIP5 Projections: Effects on Atmospheric Temperature Structure and Midlatitude Jets

2014 ◽  
Vol 27 (18) ◽  
pp. 6960-6977 ◽  
Author(s):  
Leon D. Rotstayn ◽  
Emily L. Plymin ◽  
Mark A. Collier ◽  
Olivier Boucher ◽  
Jean-Louis Dufresne ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Temperature Response ◽  
Coupled Model ◽  
Atmospheric Temperature ◽  
Temperature Structure ◽  
Tropospheric Temperature ◽  
Anthropogenic Aerosols ◽  
Aerosol Radiative Forcing ◽  
The Tropics ◽  
Aerosol Radiative

Abstract The effects of declining anthropogenic aerosols in representative concentration pathway 4.5 (RCP4.5) are assessed in four models from phase 5 the Coupled Model Intercomparison Project (CMIP5), with a focus on annual, zonal-mean atmospheric temperature structure and zonal winds. For each model, the effect of declining aerosols is diagnosed from the difference between a projection forced by RCP4.5 for 2006–2100 and another that has identical forcing, except that anthropogenic aerosols are fixed at early twenty-first-century levels. The response to declining aerosols is interpreted in terms of the meridional structure of aerosol radiative forcing, which peaks near 40°N and vanishes at the South Pole. Increasing greenhouse gases cause amplified warming in the tropical upper troposphere and strengthening midlatitude jets in both hemispheres. However, for declining aerosols the vertically averaged tropospheric temperature response peaks near 40°N, rather than in the tropics. This implies that for declining aerosols the tropospheric meridional temperature gradient generally increases in the Southern Hemisphere (SH), but in the Northern Hemisphere (NH) it decreases in the tropics and subtropics. Consistent with thermal wind balance, the NH jet then strengthens on its poleward side and weakens on its equatorward side, whereas the SH jet strengthens more than the NH jet. The asymmetric response of the jets is thus consistent with the meridional structure of aerosol radiative forcing and the associated tropospheric warming: in the NH the latitude of maximum warming is roughly collocated with the jet, whereas in the SH warming is strongest in the tropics and weakest at high latitudes.

scholarly journals Assimilation of Synthetic GOES-R ABI Infrared Brightness Temperatures and WSR-88D Radar Observations in a High-Resolution OSSE

2016 ◽  
Vol 144 (9) ◽  
pp. 3159-3180 ◽  
Author(s):  
Rebecca M. Cintineo ◽  
Jason A. Otkin ◽  
Thomas A. Jones ◽  
Steven Koch ◽  
David J. Stensrud
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Doppler Radar ◽  
Forecast Accuracy ◽  
Central Plains ◽  
Radar Observations ◽  
Brightness Temperatures ◽  
Observing System Simulation Experiment ◽  
The Impact

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95-μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4–5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated.

scholarly journals Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

2012 ◽  
Vol 19 (5) ◽  
pp. 541-557 ◽  
Author(s):  
M. Wei ◽  
M. S. F. V. De Pondeca ◽  
Z. Toth ◽  
D. Parrish
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Lanczos Method ◽  
Forecast Errors ◽  
Observation Data ◽  
Error Covariance ◽  
Observation Impact ◽  
Analysis Error ◽  
The Tropics ◽  
The Impact

Abstract. Despite the tremendous progress that has been made in data assimilation (DA) methodology, observing systems that reduce observation errors, and model improvements that reduce background errors, the analyses produced by the best available DA systems are still different from the truth. Analysis error and error covariance are important since they describe the accuracy of the analyses, and are directly related to the future forecast errors, i.e., the forecast quality. In addition, analysis error covariance is critically important in building an efficient ensemble forecast system (EFS). Estimating analysis error covariance in an ensemble-based Kalman filter DA is straightforward, but it is challenging in variational DA systems, which have been in operation at most NWP (Numerical Weather Prediction) centers. In this study, we use the Lanczos method in the NCEP (the National Centers for Environmental Prediction) Gridpoint Statistical Interpolation (GSI) DA system to look into other important aspects and properties of this method that were not exploited before. We apply this method to estimate the observation impact signals (OIS), which are directly related to the analysis error variances. It is found that the smallest eigenvalue of the transformed Hessian matrix converges to one as the number of minimization iterations increases. When more observations are assimilated, the convergence becomes slower and more eigenvectors are needed to retrieve the observation impacts. It is also found that the OIS over data-rich regions can be represented by the eigenvectors with dominant eigenvalues. Since only a limited number of eigenvectors can be computed due to computational expense, the OIS is severely underestimated, and the analysis error variance is consequently overestimated. It is found that the mean OIS values for temperature and wind components at typical model levels are increased by about 1.5 times when the number of eigenvectors is doubled. We have proposed four different calibration schemes to compensate for the missing trailing eigenvectors. Results show that the method with calibration for a small number of eigenvectors cannot pick up the observation impacts over the regions with fewer observations as well as a benchmark with a large number of eigenvectors, but proper calibrations do enhance and improve the impact signals over regions with more data. When compared with the observation locations, the method generally captures the OIS over regions with more observation data, including satellite data over the southern oceans. Over the tropics, some observation impacts may be missed due to the smaller background errors specified in the GSI, which is not related to the method. It is found that a large number of eigenvectors are needed to retrieve impact signals that resemble the banded structures from satellite observations, particularly over the tropics. Another benefit from the Lanczos method is that the dominant eigenvectors can be used in preconditioning the conjugate gradient algorithm in the GSI to speed up the convergence.

scholarly journals Predictability and Genesis of Hurricane Karl (2010) Examined through the EnKF Assimilation of Field Observations Collected during PREDICT

2014 ◽  
Vol 71 (4) ◽  
pp. 1260-1275 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Control Experiment ◽  
Initial Condition ◽  
Field Observations ◽  
Ensemble Forecasts ◽  
Thermodynamic Structure ◽  
Cloud Systems ◽  
The Tropics

Abstract The genesis of Hurricane Karl (2010) is explored using analyses and forecasts from a cycling ensemble Kalman filter (EnKF) that assimilates routinely collected observations as well as dropsonde measurements that were taken during the Pre-Depression Investigation of Cloud Systems in the Tropics (PREDICT) field campaign. A total of 127 dropsonde observations were collected from six PREDICT flight missions over a 5-day period before and during Karl’s genesis. EnKF analyses that take into account the PREDICT dropsondes provide a detailed four-dimensional overview of the evolving kinematic and thermodynamic structure within the pregenesis disturbance. In particular, the additional field observations are found to increase the low- and midlevel circulation and column moisture in the EnKF analyses and reduce the position error of the low-level vortex. Deterministic forecasts from these analyses show a 24-h improvement in predicting genesis over a control experiment that omits the dropsonde observations. In ensemble forecasts for this event, the more accurate analyses translate into a higher confidence in predicting the intensification of Karl; that is, data assimilation experiments also suggest that initial condition errors at the mesoscale pose large challenges for predicting genesis, thus highlighting the need for improved observation networks and more advanced data assimilation methods.

Sign in / Sign up

Export Citation Format

Share Document