Adjoint-Derived Observation Impact Using WRF in the Western North Pacific

2013 ◽  
Vol 141 (11) ◽  
pp. 4080-4097 ◽  
Author(s):  
Byoung-Joo Jung ◽  
Hyun Mee Kim ◽  
Thomas Auligné ◽  
Xin Zhang ◽  
Xiaoyan Zhang ◽  
...  
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Forecast Error ◽  
Weather Prediction ◽  
Motion Vector ◽  
Background Error ◽  
Error Covariance ◽  
Observation Impact ◽  
The Impact ◽  
Total Impact

Abstract An increasing number of observations have contributed to the performance of numerical weather prediction systems. Accordingly, it is important to evaluate the impact of these observations on forecast accuracy. While the observing system experiment (OSE) requires considerable computational resources, the adjoint-derived method can evaluate the impact of all observational components at a lower cost. In this study, the effect of observations on forecasts is evaluated by the adjoint-derived method using the Weather Research and Forecasting Model, its adjoint model, and a corresponding three-dimensional variational data assimilation system in East Asia and the western North Pacific for the 2008 typhoon season. Radiance observations had the greatest total impact on forecasts, but conventional wind observations had the greatest impact per observation. For each observation type, the total impact was greatest for radiosonde and each Advanced Microwave Sounding Unit (AMSU)-A satellite, followed by surface synoptic observation from a land station (SYNOP), Quick Scatterometer (QuikSCAT), atmospheric motion vector (AMV) wind from a geostationary satellite (GEOAMV), and aviation routine weather reports (METARs). The fraction of beneficial observations was approximately 60%–70%, which is higher than that reported in previous studies. For several analyses of Typhoons Sinlaku (200813) and Jangmi (200815), dropsonde soundings taken near the typhoon had similar or greater observation impacts than routine radiosonde soundings. The sensitivity to the error covariance parameter indicates that reducing (increasing) observation (background) error covariance helps to reduce forecast error in the current analysis framework. The observation impact from OSEs is qualitatively similar to that from the adjoint method for major observation types. This study confirms that radiosonde observations provide primary information on the atmospheric state as in situ observations and that satellite radiances are an essential component of atmospheric observation systems.

scholarly journals The Impact of Assimilating Satellite Radiance Observations in the Copernicus European Regional Reanalysis (CERRA)

2021 ◽  
Vol 13 (3) ◽  
pp. 426
Author(s):  
Zheng Qi Wang ◽  
Roger Randriamampianina
Keyword(s):  
Weather Prediction ◽  
Deterministic System ◽  
Observation Error ◽  
Key Factors ◽  
Background Error ◽  
Current Configuration ◽  
Error Covariance ◽  
Regional Reanalysis ◽  
The Impact ◽  
European Regional

The assimilation of microwave and infrared (IR) radiance satellite observations within numerical weather prediction (NWP) models have been an important component in the effort of improving the accuracy of analysis and forecast. Such capabilities were implemented during the development of the high-resolution Copernicus European Regional Reanalysis (CERRA), funded by the Copernicus Climate Change Services (C3S). The CERRA system couples the deterministic system with the ensemble data assimilation to provide periodic updates of the background error covariance matrix. Several key factors for the assimilation of radiances were investigated, including appropriate use of variational bias correction (VARBC), surface-sensitive AMSU-A observations and observation error correlation. Twenty-one-day impact studies during the summer and winter seasons were conducted. Generally, the assimilation of radiances has a small impact on the analysis, while greater impacts are observed on short-range (12 and 24-h) forecasts with an error reduction of 1–2% for the mid and high troposphere. Although, the current configuration provided less accurate forecasts from 09 and 18 UTC analysis times. With the increased thinning distances and the rejection of IASI observation over land, the errors in the analyses and 3 h forecasts on geopotential height were reduced up to 2%.

Evaluating Ensemble Kalman Filter Analyses of Severe Hailstorms on 8 May 2017 in Colorado: Effects of State Variable Updating and Multimoment Microphysics Schemes on State Variable Cross Covariances

2020 ◽  
Vol 148 (6) ◽  
pp. 2365-2389
Author(s):  
Jonathan Labriola ◽  
Nathan Snook ◽  
Youngsun Jung ◽  
Ming Xue
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Forecast Error ◽  
Variable Cross ◽  
Background Error ◽  
Mixing Ratios ◽  
State Variable ◽  
Error Covariance ◽  
Thermodynamic Variables ◽  
The Impact

Abstract Ensemble Kalman filter (EnKF) analyses of the storms associated with the 8 May 2017 Colorado severe hail event using either the Milbrandt and Yau (MY) or the NSSL double-moment bulk microphysics scheme in the forecast model are evaluated. With each scheme, two experiments are conducted in which the reflectivity (Z) observations update in addition to dynamic and thermodynamic variables: 1) only the hydrometeor mixing ratios or 2) all microphysical variables. With fewer microphysical variables directly constrained by the Z observations, only updating hydrometeor mixing ratios causes the forecast error covariance structure to become unreliable, and results in larger errors in the analysis. Experiments that update all microphysical variables produce analyses with the lowest Z root-mean-square innovations; however, comparing the estimated hail size against hydrometeor classification algorithm output suggests that further constraint from observations is needed to more accurately estimate surface hail size. Ensemble correlation analyses are performed to determine the impact of hail growth assumptions in the MY and NSSL schemes on the forecast error covariance between microphysical and thermodynamic variables. In the MY scheme, Z is negatively correlated with updraft intensity because the strong updrafts produce abundant small hail aloft. The NSSL scheme predicts the growth of large hail aloft; consequently, Z is positively correlated with storm updraft intensity and hail state variables. Hail production processes are also shown to alter the background error covariance for liquid and frozen hydrometeor species. Results in this study suggest that EnKF analyses are sensitive to the choice of MP scheme (e.g., the treatment of hail growth processes).

scholarly journals Forecast Sensitivity Observation Impact in the 4DVAR and Hybrid-4DVAR Data Assimilation Systems

2019 ◽  
Vol 36 (8) ◽  
pp. 1563-1575 ◽  
Author(s):  
Sung-Min Kim ◽  
Hyun Mee Kim
Keyword(s):  
Data Assimilation ◽  
East Asia ◽  
Forecast Error ◽  
Integration Time ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Error Covariance ◽  
Atmospheric Motion ◽  
Observation Impact ◽  
Atmospheric Motion Vectors

AbstractIn this study, the observation impacts on 24-h forecast error reduction (FER), based on the adjoint method in the four-dimensional variational (4DVAR) data assimilation (DA) and hybrid-4DVAR DA systems coupled with the Unified Model, were evaluated from 0000 UTC 5 August to 1800 UTC 26 August 2014. The nonlinear FER in hybrid-4DVAR was 12.2% greater than that in 4DVAR due to the use of flow-dependent background error covariance (BEC), which was a weighted combination of the static BEC and the ensemble BEC based on ensemble forecasts. In hybrid-4DVAR, the observation impacts (i.e., the approximated nonlinear FER) for most observation types increase compared to those in 4DVAR. The increased observation impact from using hybrid-4DVAR instead of 4DVAR changes depending on the analysis time and regions. To calculate the ensemble BEC in hybrid-4DVAR, analyses at 0600 and 1800 UTC (0000 and 1200 UTC) used 3-h (9-h) ensemble forecasts. Greater observation impact was obtained when 3-h ensemble forecasts were used for the ensemble BEC at 0600 and 1800 UTC, than with 9-h ensemble forecasts at 0000 and 1200 UTC. Different from other observations, the atmospheric motion vectors (AMVs) deduced from geostationary satellite are more frequently observed in the same area. When the ensemble forecasts with longer integration times were used for the ensemble BEC in hybrid-4DVAR, the observation impact of the AMVs decreased the most in East Asia. This implies that the observation impact of AMVs in East Asia shows the highest sensitivity to the integration time of the ensemble members used for deducing the flow-dependent BEC in hybrid-4DVAR.

scholarly journals Operational Performance of a New Barotropic Model (WBAR) in the Western North Pacific Basin

2006 ◽  
Vol 21 (4) ◽  
pp. 656-662 ◽  
Author(s):  
Charles R. Sampson ◽  
James S. Goerss ◽  
Harry C. Weber
Keyword(s):  
North Pacific ◽  
Western North Pacific ◽  
Prediction Models ◽  
Weather Prediction ◽  
Track Forecast ◽  
Forecast Performance ◽  
Barotropic Model ◽  
The North ◽  
Forecasting System ◽  
The Impact

Abstract The Weber barotropic model (WBAR) was originally developed using predefined 850–200-hPa analyses and forecasts from the NCEP Global Forecasting System. The WBAR tropical cyclone (TC) track forecast performance was found to be competitive with that of more complex numerical weather prediction models in the North Atlantic. As a result, WBAR was revised to incorporate the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecasts for use at the Joint Typhoon Warning Center (JTWC). The model was also modified to analyze its own storm-dependent deep-layer mean fields from standard NOGAPS pressure levels. Since its operational installation at the JTWC in May 2003, WBAR TC track forecast performance has been competitive with the performance of other more complex NWP models in the western North Pacific. Its TC track forecast performance combined with its high availability rate (93%–95%) has warranted its inclusion in the JTWC operational consensus. The impact of WBAR on consensus TC track forecast performance has been positive and WBAR has added to the consensus forecast availability (i.e., having at least two models to provide a consensus forecast).

Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction

2021 ◽  
Author(s):  
Deming Meng ◽  
Yaodeng Chen ◽  
Jun Li ◽  
Hongli Wang ◽  
Yuanbing Wang ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Background Error ◽  
Single Observation ◽  
Numerical Weather ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Analysis Scheme ◽  
The Impact

AbstractThe background error covariance (B) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and three-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analyses and forecasts bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.

Retrieval of Moisture from Simulated GPS Slant-Path Water Vapor Observations Using 3DVAR with Anisotropic Recursive Filters

2007 ◽  
Vol 135 (4) ◽  
pp. 1506-1521 ◽  
Author(s):  
Haixia Liu ◽  
Ming Xue ◽  
R. James Purser ◽  
David F. Parrish
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Background Error ◽  
Special Configuration ◽  
Background Error Covariance ◽  
Recursive Filters ◽  
Error Covariance ◽  
Slant Path ◽  
The Impact

Abstract Anisotropic recursive filters are implemented within a three-dimensional variational data assimilation (3DVAR) framework to efficiently model the effect of flow-dependent background error covariance. The background error covariance is based on an estimated error field and on the idea of Riishøjgaard. In the anisotropic case, the background error pattern can be stretched or flattened in directions oblique to the alignment of the grid coordinates and is constructed by applying, at each point, six recursive filters along six directions corresponding, in general, to a special configuration of oblique lines of the grid. The recursive filters are much more efficient than corresponding explicit filters used in an earlier study and are therefore more suitable for real-time numerical weather prediction. A set of analysis experiments are conducted at a mesoscale resolution to examine the effectiveness of the 3DVAR system in analyzing simulated global positioning system (GPS) slant-path water vapor observations from ground-based GPS receivers and observations from collocated surface stations. It is shown that the analyses produced with recursive filters are at least as good as those with corresponding explicit filters. In some cases, the recursive filters actually perform better. The impact of flow-dependent background errors modeled using the anisotropic recursive filters is also examined. The use of anisotropic filters improves the analysis, especially in terms of finescale structures. The analysis system is found to be effective in the presence of typical observational errors. The sensitivity of isotropic and anisotropic recursive-filter analyses to the decorrelation scales is also examined systematically.

Impact of Removing Covariance Localization in an Ensemble Kalman Filter: Experiments with 10 240 Members Using an Intermediate AGCM

2016 ◽  
Vol 144 (12) ◽  
pp. 4849-4865 ◽  
Author(s):  
Keiichi Kondo ◽  
Takemasa Miyoshi
Keyword(s):  
Kalman Filter ◽  
Long Range ◽  
Ensemble Kalman Filter ◽  
Sampling Error ◽  
Forecast Error ◽  
Forecast Accuracy ◽  
Large Sample Size ◽  
Background Error ◽  
Error Covariance ◽  
The Impact

Abstract The ensemble Kalman filter (EnKF) with high-dimensional geophysical systems usually employs up to 100 ensemble members and requires covariance localization to reduce the sampling error in the forecast error covariance between distant locations. The authors’ previous work pioneered implementation of an EnKF with a large ensemble of up to 10 240 members, but this method required application of a relatively broad covariance localization to avoid memory overflow. This study modified the EnKF code to save memory and enabled for the first time the removal of completely covariance localization with an intermediate AGCM. Using the large sample size, this study aims to investigate the analysis and forecast accuracy, as well as the impact of covariance localization when the sampling error is small. A series of 60-day data assimilation cycle experiments with different localization scales are performed under the perfect model scenario to investigate the pure impact of covariance localization. The results show that the analysis and 7-day forecasts are much improved by removing covariance localization and that the long-range covariance between distant locations plays a key role. The eigenvectors of the background error covariance matrix based on the 10 240 ensemble members are explicitly computed and reveal long-range structures.

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 Effect of Enhanced Satellite-Derived Atmospheric Motion Vectors on Numerical Weather Prediction in East Asia Using an Adjoint-Based Observation Impact Method

2017 ◽  
Vol 32 (2) ◽  
pp. 579-594 ◽  
Author(s):  
Myunghwan Kim ◽  
Hyun Mee Kim ◽  
JinWoong Kim ◽  
Sung-Min Kim ◽  
Christopher Velden ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Forecast Error ◽  
Weather Prediction ◽  
Motion Vectors ◽  
Numerical Weather ◽  
Atmospheric Motion ◽  
Observation Impact ◽  
Total Observation ◽  
Atmospheric Motion Vectors ◽  
The Impact

Abstract When producing forecasts by integrating a numerical weather prediction model from an analysis, not all observations assimilated into the analysis improve the forecast. Therefore, the impact of particular observations on the forecast needs to be evaluated quantitatively to provide relevant information about the impact of the observing system. One way to assess the observation impact is to use an adjoint-based method that estimates the impact of each assimilated observation on reducing the error of the forecast. In this study, the Weather Research and Forecasting Model and its adjoint are used to evaluate the impact of several types of observations, including enhanced satellite-derived atmospheric motion vectors (AMVs) that were made available during observation campaigns for two typhoons: Sinlaku and Jangmi, which both formed in the western North Pacific during September 2008. Without the assimilation of enhanced AMV data, radiosonde observations and satellite radiances show the highest total observation impact on forecasts. When enhanced AMVs are included in the assimilation, the observation impact of AMVs is increased and the impact of radiances is decreased. The highest ratio of beneficial observations comes from GPS Precipitable Water (GPSPW) without the assimilation of enhanced AMVs. Most observations express a ratio of approximately 60%. Enhanced AMVs improve forecast fields when tracking the typhoon centers of Sinlaku and Jangmi. Both the model background and the analysis are improved by the continuous cycling of enhanced AMVs, with a greater reduction in forecast error along the background trajectory than the analysis trajectory. Thus, while the analysis–forecast system is improved by assimilating these observations, the total observation impact is smaller as a result of the improvement.

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