scholarly journals Variational Bias Correction of TAMDAR Temperature Observations in the WRF Data Assimilation System

2019 ◽  
Vol 147 (6) ◽  
pp. 1927-1945
Author(s):  
Feng Gao ◽  
Zhiquan Liu ◽  
Juhui Ma ◽  
Neil A. Jacobs ◽  
Peter P. Childs ◽  
...  
Keyword(s):  
Mach Number ◽  
Data Assimilation ◽  
Bias Correction ◽  
Short Range ◽  
Forecast Error ◽  
Meteorological Data ◽  
Precipitation Event ◽  
Average Error ◽  
Bias Model ◽  
Range Forecasts

Abstract A variational bias correction (VarBC) scheme is developed and tested using regional Weather Research and Forecasting Model Data Assimilation (WRFDA) to correct systematic errors in aircraft-based measurements of temperature produced by the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) system. Various bias models were investigated, using one or all of aircraft height tendency, Mach number, temperature tendency, and the observed temperature as predictors. These variables were expected to account for the representation of some well-known error sources contributing to uncertainties in TAMDAR temperature measurements. The parameters corresponding to these predictors were evolved in the model for a two-week period to generate initial estimates according to each unique aircraft tail number. Sensitivity experiments were then conducted for another one-month period. Finally, a case study using VarBC of a cold front precipitation event is examined. The implementation of VarBC reduces biases in TAMDAR temperature innovations. Even when using a bias model containing a single predictor, such as height tendency or Mach number, the VarBC produces positive impacts on analyses and short-range forecasts of temperature with smaller standard deviations and biases than the control run. Additionally, by employing a multiple-predictor bias model, which describes the statistical relations between innovations and predictors, and uses coefficients to control the evolution of components in the bias model with respect to their reference values, VarBC further reduces the average error of analyses and short-range forecasts with respect to observations. The potential impacts of VarBC on precipitation forecasts were evaluated, and the VarBC is able to indirectly improve the prediction of precipitation location by reducing the forecast error for wind-related synoptic circulation leading to precipitation.

scholarly journals Variational Correction of Aircraft Temperature Bias in the NCEP’s GSI Analysis System

2015 ◽  
Vol 143 (9) ◽  
pp. 3774-3803 ◽  
Author(s):  
Yanqiu Zhu ◽  
John C. Derber ◽  
R. James Purser ◽  
Bradley A. Ballish ◽  
Jeffrey Whiting
Keyword(s):  
Bias Correction ◽  
Meteorological Data ◽  
Vertical Displacement ◽  
Radiosonde Data ◽  
Temperature Bias ◽  
Bias Model ◽  
Analysis System ◽  
Environmental Prediction ◽  
Time Information ◽  
Interpolation Analysis

Abstract Various studies have noted that aircraft temperature data have a generally warm bias relative to radiosonde data around 200 hPa. In this study, variational aircraft temperature bias correction is incorporated in the Gridpoint Statistical Interpolation analysis system at the National Centers for Environmental Prediction. Several bias models, some of which include information about aircraft ascent/descent rate, are investigated. The results show that the aircraft temperature bias correction cools down the atmosphere analysis around 200 hPa, and improves the analysis and forecast fits to the radiosonde data. Overall, the quadratic aircraft ascent/descent rate bias model performs better than other bias models tested here, followed closely by the aircraft ascent/descent rate bias model. Two other issues, undocumented in previous studies, are also discussed in this paper. One is the bias correction of aircraft report (AIREP) data. Unlike Aircraft Meteorological Data Relay (AMDAR) data, where unique corrections are applied for each aircraft, bias correction is applied indiscriminately (without regard to tail numbers) to all AIREP data. The second issue is the problem of too many aircraft not reporting time in seconds, or too infrequently, to be able to determine accurate vertical displacement rates. In addition to the finite-difference method employed to estimate aircraft ascent/descent rate, a tensioned-splines method is tested to obtain more continuously smooth aircraft ascent/descent rates and mitigate the missing time information.

scholarly journals The Inclusion of IWV Estimates from AIRS/AMSU and SSM/I Sensors into the CPTEC/INPE Global Data Assimilation System

2013 ◽  
Vol 141 (1) ◽  
pp. 93-111
Author(s):  
Luiz F. Sapucci ◽  
Dirceu L. Herdies ◽  
Renata W. B. Mendonça
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Southern Hemisphere ◽  
Short Range ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Range Forecasts

Abstract Water vapor plays a crucial role in atmospheric processes and its distribution is associated with cloud-cover fraction and rainfall. The inclusion of integrated water vapor (IWV) estimates in numerical weather prediction improves the vertical structure of the humidity analysis and consequently contributes to obtaining a more realistic atmospheric state. Currently, satellite remote sensing is the most important source of humidity measurements in the Southern Hemisphere, providing information with good horizontal resolution and global coverage. In this study, the inclusion of IWV retrieved from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU) and Special Sensor Microwave Imager (SSM/I) were investigated as additional information in the Physical-space Statistical Analysis System (PSAS), which is the operational data assimilation system at the Center for Weather Forecasting and Climate Studies of the Brazilian National Institute for Space Research (CPTEC/INPE). Experiments were carried out with and without the assimilation of IWV values from both sensors. Results show that, in general, the IWV assimilation reduces the error in short-range forecasts of humidity profile, particularly over tropical regions. In these experiments, an analysis of the impact of the inclusion of IWV values from SSM/I and AIRS/AMSU sensors was done. Results indicated that the impact of the SSM/I values is significant over high-latitude oceanic regions in the Southern Hemisphere, while the impact of AIRS/AMSU values is more significant over continental regions where surface measurements are scarce, such as the Amazonian region. In that area the assimilation of IWV values from the AIRS/AMSU sensor shows a tendency to reduce the overestimate of the precipitation in short-range forecasts.

Assimilation of SSM/I Radiances in the NCEP Global Data Assimilation System

2006 ◽  
Vol 134 (9) ◽  
pp. 2612-2631 ◽  
Author(s):  
Kozo Okamoto ◽  
John C. Derber
Keyword(s):  
Wind Speed ◽  
Data Assimilation ◽  
Bias Correction ◽  
Surface Wind ◽  
Radiative Transfer Model ◽  
Average Error ◽  
Transfer Model ◽  
The Tropics ◽  
Radiance Data ◽  
Global Data

Abstract A technique for the assimilation of Special Sensor Microwave Imager (SSM/I) data in the National Centers for Environmental Prediction (NCEP) global data assimilation and forecast system is described. Because the radiative transfer model used does not yet allow for cloud/rain effects, it is crucial to properly identify and exclude (or correct) cloud/rain-contaminated radiances using quality control (QC) and bias correction procedures. The assimilation technique is unique in that both procedures take into account the effect of the liquid cloud on the difference between observed and simulated brightness temperature for each SSM/I channel. The estimate of the total column cloud liquid water from observed radiances is used in a frequency-dependent cloud detection component of the QC and as a predictor in the bias correction algorithm. Also, a microwave emissivity Jacobian model with respect to wind speed is developed for oceanic radiances. It was found that the surface wind information in the radiance data can be extracted through the emissivity model Jacobian rather than producing and including a separate SSM/I wind speed retrieval. A two-month-long data assimilation experiment from July to August 2004 using NCEP’s Gridpoint Statistical Interpolation analysis system and the NCEP operational forecast model was performed. In general, the assimilation of SSM/I radiance has a significant positive impact on the analyses and forecasts. Moisture is added in the Northern Hemisphere and Tropics and is slightly reduced in the Southern Hemisphere. The moisture added appears to be slightly excessive in the Tropics verified against rawinsonde observations. Nevertheless, the assimilation of SSM/I radiance data reduces model spinup of precipitation and substantially improves the dynamic fields, especially in measures of the vector wind error at 200 hPa in the Tropics. In terms of hurricane tracks, SSM/I radiance assimilation produces more cases with smaller errors and reduces the average error. No disruption of the Hadley circulation is found from the introduction of the SSM/I radiance data.

scholarly journals A fast, single-iteration ensemble Kalman smoother for sequential data assimilation

2021 ◽  
Author(s):  
Colin Grudzien ◽  
Marc Bocquet
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Short Range ◽  
Forecast Error ◽  
Cost Effective ◽  
Sequential Data ◽  
Loop Optimization ◽  
Weakly Nonlinear ◽  
Kalman Smoother ◽  
Error Dynamics

Abstract. Ensemble-variational methods form the basis of the state-of-the-art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for reducing prediction error in online, short-range forecast systems. We propose a novel, outer-loop optimization of the ensemble-variational formalism for applications in which forecast error dynamics are weakly nonlinear, such as synoptic meteorology. In order to rigorously derive our method and demonstrate its novelty, we review ensemble smoothers that appear throughout the literature in a unified Bayesian maximum-a-posteriori narrative, updating and simplifying some results. After mathematically deriving our technique, we systematically develop and inter-compare all studied schemes in the open-source Julia package DataAssimilationBenchmarks.jl, with pseudo-code provided for these methods. This high-performance numerical framework, supporting our mathematical results, produces extensive benchmarks that demonstrate the significant performance advantages of our proposed technique. In particular, our single-iteration ensemble Kalman smoother is shown both to improve prediction / posterior accuracy and to simultaneously reduce the leading order cost of iterative, sequential smoothers in a variety of relevant test cases for operational short-range forecasts. This long work is thus intended to present our novel single-iteration ensemble Kalman smoother, and to provide a theoretical and computational framework for the study of sequential, ensemble-variational Kalman filters and smoothers generally.

scholarly journals Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts

2019 ◽  
Vol 20 (7) ◽  
pp. 1379-1398 ◽  
Author(s):  
Shasha Han ◽  
Paulin Coulibaly
Keyword(s):  
Bias Correction ◽  
Short Range ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Hydrologic Uncertainty ◽  
Ensemble Weather Forecasts ◽  
Range Forecasts

Recent advances in the field of flood forecasting have shown increased interests in probabilistic forecasting as it provides not only the point forecast but also the assessment of associated uncertainty. Here, an investigation of a hydrologic uncertainty processor (HUP) as a postprocessor of ensemble forecasts to generate probabilistic flood forecasts is presented. The main purpose is to quantify dominant uncertainties and enhance flood forecast reliability. HUP is based on Bayes’s theorem and designed to capture hydrologic uncertainty. Ensemble forecasts are forced by ensemble weather forecasts from the Global Ensemble Prediction System (GEPS) that are inherently uncertain, and the input uncertainty propagates through the model chain and integrates with hydrologic uncertainty in HUP. The bias of GEPS was removed using multivariate bias correction, and several scenarios were developed by different combinations of GEPS with HUP. The performance of different forecast horizons for these scenarios was compared using multifaceted evaluation metrics. Results show that HUP is able to improve the performance for both short- and medium-range forecasts; the improvement is significant for short lead times and becomes less obvious with increasing lead time. Overall, the performances for short-range forecasts when using HUP are promising, and the most satisfactory result for the short range is obtained by applying bias correction to each ensemble member plus applying the HUP postprocessor.

New operational nowcasting system at Finnish Meteorological Institute

2021 ◽  
Author(s):  
Leila Hieta ◽  
Mikko Partio ◽  
Marko Laine ◽  
Marja-Liisa Tuomola ◽  
Harri Hohti ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Short Range ◽  
Forecast Error ◽  
Weather Prediction ◽  
Multiple Sources ◽  
Finnish Meteorological Institute ◽  
Operational Forecast ◽  
Meteorological Institute ◽  
Range Forecasts

<p>Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI). The system combines information from multiple sources to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The information sources combined are 1) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 2) radar-based nowcast 3) 10-day operational forecast. The Smartmet nowcast is currently produced for parameters 2-m temperature, 10-m wind speed, relative humidity, total cloud cover and accumulated 1-hour precipitation.</p><p>The system produces hourly updating nowcast information over the Scandinavian forecast domain and combines it seamlessly with the 10-day operational forecast information. Prior the combination a simple bias correction scheme based on recent forecast error information is applied to MNWC model analysis and forecast fields of 2-m temperature, relative humidity and 10-m wind speed. The blending of the nowcast and the 10-day operational forecast information is done using Optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable.</p><p>FMI has operationally produced Smartmet nowcast forecasts since September 2020. The validation of the data is in progress. The available results show that the Smartmet nowcast is improving the quality of short range forecasts and producing seamless and consistent forecasts. The method is also reducing the delay of forecast production. The Smartmet nowcast method will be automated in FMI forecast production in the near future.</p>

The impact of NAWDEX dropsonde and extra radiosonde observations on forecast quality and tropopause structure

2020 ◽  
Author(s):  
Matthias Schindler ◽  
Martin Weissmann ◽  
Andreas Schäfler ◽  
Gabor Radnoti
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Short Range ◽  
Forecast Error ◽  
Tropical Storm ◽  
Wind Speeds ◽  
Observation Space ◽  
The Impact ◽  
Forecast Quality

<p>Utilizing a multitude of in situ and remote sensing instruments, a comprehensive dataset was collected during the transatlantic field campaign NAWDEX in autumn 2016. Cycled data denial experiments with the global model of the ECMWF showed that additionally collected dropsonde and radiosonde observations contributed to a reduction in the short-range forecast error, with the most prominent error reductions being linked to Tropical Storm Karl, cyclones Matthew and Nicole and their subsequent interaction with the midlatitude waveguide. While the short-range forecast quality was improved, Schäfler et al. (2019, in review) demonstrated that ECMWF IFS analyses exhibit deficiencies in capturing observed wind speeds at and above the dynamical tropopause during NAWDEX. Therefore, data assimilation output from the ECMWF IFS is used to evaluate the observational influence on the tropopause. Statistics of data assimilation diagnostics such as the analysis increment and first guess departure will be assessed in observation space in a tropopause relative framework to quantify the impact of assimilated radiosonde observations on tropopause location and sharpness.</p>

scholarly journals Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

2005 ◽  
Vol 133 (8) ◽  
pp. 2310-2334 ◽  
Author(s):  
Anna Borovikov ◽  
Michele M. Rienecker ◽  
Christian L. Keppenne ◽  
Gregory C. Johnson
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Forecast Error ◽  
Independent Set ◽  
Velocity Fields ◽  
Optimal Interpolation ◽  
Model Errors ◽  
Error Statistics ◽  
Practical Applications ◽  
Monte Carlo Techniques

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

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