Verification of 24-h Quantitative Precipitation Forecasts over the Pacific Northwest from a High-Resolution Ensemble Kalman Filter System

Abstract Environment and Climate Change Canada (ECCC) has recently developed an experimental high-resolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.

Download Full-text

Data Assimilation of the High-Resolution Sea Surface Temperature Obtained from the Aqua-Terra Satellites (MODIS-SST) Using an Ensemble Kalman Filter

Remote Sensing ◽

10.3390/rs5063123 ◽

2013 ◽

Vol 5 (6) ◽

pp. 3123-3139 ◽

Cited By ~ 13

Author(s):

Yasumasa Miyazawa ◽

Hiroshi Murakami ◽

Toru Miyama ◽

Sergey Varlamov ◽

Xinyu Guo ◽

...

Keyword(s):

Kalman Filter ◽

High Resolution ◽

Data Assimilation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Ensemble Kalman Filter ◽

Sea Surface

Download Full-text

Evaluation of Surface Analyses and Forecasts with a Multiscale Ensemble Kalman Filter in Regions of Complex Terrain

Monthly Weather Review ◽

10.1175/2010mwr3612.1 ◽

2011 ◽

Vol 139 (6) ◽

pp. 2008-2024 ◽

Cited By ~ 24

Author(s):

Brian C. Ancell ◽

Clifford F. Mass ◽

Gregory J. Hakim

Keyword(s):

Kalman Filter ◽

High Resolution ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Complex Terrain ◽

Error Variance ◽

Small Scale ◽

Observation Error ◽

Grid Spacing ◽

Background Error

Abstract Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

Download Full-text

Initial perturbations based on Ensemble Transfrom Kalman Filter with rescaling method for ensemble forecast

Weather and Forecasting ◽

10.1175/waf-d-20-0176.1 ◽

2021 ◽

Author(s):

Jingzhuo Wang ◽

Jing Chen ◽

Hanbin Zhang ◽

Hua Tian ◽

Yining Shi

Keyword(s):

Growth Rate ◽

Kalman Filter ◽

Weather Forecasting ◽

Initial Perturbation ◽

Ensemble Forecast ◽

Ensemble Prediction ◽

Forecast Errors ◽

Model Uncertainties ◽

Ensemble Prediction System ◽

Probabilistic Forecasts

AbstractEnsemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

Download Full-text

Intercomparison of an Ensemble Kalman Filter with Three- and Four-Dimensional Variational Data Assimilation Methods in a Limited-Area Model over the Month of June 2003

Monthly Weather Review ◽

10.1175/2010mwr3610.1 ◽

2011 ◽

Vol 139 (2) ◽

pp. 566-572 ◽

Cited By ~ 48

Author(s):

Meng Zhang ◽

Fuqing Zhang ◽

Xiang-Yu Huang ◽

Xin Zhang

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Forecast Error ◽

Wrf Model ◽

Warm Season ◽

Variational Data Assimilation ◽

Lead Times ◽

Limited Area ◽

Forecast Errors

Abstract This study compares the performance of an ensemble Kalman filter (EnKF) with both the three-dimensional and four-dimensional variational data assimilation (3DVar and 4DVar) methods of the Weather Research and Forecasting (WRF) model over the contiguous United States in a warm-season month (June) of 2003. The data assimilated every 6 h include conventional sounding and surface observations as well as data from wind profilers, ships and aircraft, and the cloud-tracked winds from satellites. The performances of these methods are evaluated through verifying the 12- to 72-h forecasts initialized twice daily from the analysis of each method against the standard sounding observations. It is found that 4DVar has consistently smaller error than that of 3DVar for winds and temperature at all forecast lead times except at 60 and 72 h when their forecast errors become comparable in amplitude, while the two schemes have similar performance in moisture at all lead times. The forecast error of the EnKF is comparable to that of the 4DVar at 12–36-h lead times, both of which are substantially smaller than that of the 3DVar, despite the fact that 3DVar fits the sounding observations much more closely at the analysis time. The advantage of the EnKF becomes even more evident at 48–72-h lead times; the 72-h forecast error of the EnKF is comparable in magnitude to the 48-h error of 3DVar/4DVar.

Download Full-text

Model Error Representation in an Operational Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/2008mwr2737.1 ◽

2009 ◽

Vol 137 (7) ◽

pp. 2126-2143 ◽

Cited By ~ 137

Author(s):

P. L. Houtekamer ◽

Herschel L. Mitchell ◽

Xingxiu Deng

Keyword(s):

Kalman Filter ◽

Ensemble Kalman Filter ◽

Model Error ◽

Ensemble Prediction ◽

Isotropic Model ◽

Error Sources ◽

Ensemble Prediction System ◽

Stochastic Perturbations ◽

Ensemble Mean ◽

The Impact

Since 12 January 2005, an ensemble Kalman filter (EnKF) has been used operationally at the Meteorological Service of Canada to provide the initial conditions for the medium-range forecasts of the ensemble prediction system. One issue in EnKF development is how to best account for model error. It is shown that in a perfect-model environment, without any model error or model error simulation, the EnKF spread remains representative of the ensemble mean error with respect to a truth integration. Consequently, the EnKF can be used to quantify the impact of the various error sources in a data-assimilation cycle on the quality of the ensemble mean. Using real rather than simulated observations, but still not simulating model error in any manner, the rms ensemble spread is found to be too small by approximately a factor of 2. It is then attempted to account for model error by using various combinations of the following four different approaches: (i) additive isotropic model error perturbations; (ii) different versions of the model for different ensemble members; (iii) stochastic perturbations to physical tendencies; and (iv) stochastic kinetic energy backscatter. The addition of isotropic model error perturbations is found to have the biggest impact. The identification of model error sources could lead to a more realistic, likely anisotropic, parameterization. Using different versions of the model has a small but clearly positive impact and consequently both (i) and (ii) are used in the operational EnKF. The use of approaches (iii) and (iv) did not lead to further improvements.

Download Full-text

Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE

Weather and Forecasting ◽

10.1175/waf-d-11-00101.1 ◽

2012 ◽

Vol 27 (4) ◽

pp. 988-1002 ◽

Cited By ~ 36

Author(s):

Sabrina Bentzien ◽

Petra Friederichs

Keyword(s):

High Resolution ◽

Mixture Models ◽

Generalized Pareto Distribution ◽

Rain Gauge ◽

Ensemble Prediction ◽

Small Scale ◽

Inverse Gaussian ◽

Predictive Distributions ◽

Ensemble Prediction System ◽

Quantitative Precipitation Forecasts

Abstract Statistical postprocessing is an integral part of an ensemble prediction system. This study compares methods used to derive probabilistic quantitative precipitation forecasts based on the high-resolution version of the German-focused Consortium for Small-Scale Modeling (COSMO-DE) time-lagged ensemble (COSMO-DE-TLE). The investigation covers the period from July 2008 to June 2011 for a region over northern Germany with rain gauge measurements from 445 stations. The investigated methods provide pointwise estimates of the predictive distribution using logistic and quantile regression, and full predictive distributions using parametric mixture models. All mixture models use a point mass at zero to represent the probability of precipitation. The amount of precipitation is modeled by either a gamma, lognormal, or inverse Gaussian distribution. Furthermore, an adaptive tail using a generalized Pareto distribution (GPD) accounts for a better representation of extreme precipitation. The predictive probabilities, quantiles, and distributions are evaluated using the Brier, the quantile verification, and the continuous ranked probability scores. Baseline predictions and covariates are based on first-guess estimates from the COSMO-DE-TLE. Predictive performance is largely improved by statistical postprocessing due to an increase in reliability and resolution. The mixture models show some deficiencies. The inverse Gaussian fails to provide calibrated predictive distributions, whereas the lognormal and gamma mixtures perform well within the bulk of the distribution. Both mixtures provide significantly less skill for the extremal quantiles (0.99–0.999). Their representation is largely improved by incorporating an adaptive GPD tail. Even more stable estimates are obtained if the annual cycle is included in the postprocessing and training is performed on almost 3 yr of data.

Download Full-text

A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation

Monthly Weather Review ◽

10.1175/mwr3021.1 ◽

2005 ◽

Vol 133 (11) ◽

pp. 3081-3094 ◽

Cited By ~ 133

Author(s):

A. Caya ◽

J. Sun ◽

C. Snyder

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Forecast Model ◽

Radar Data ◽

Forecast Errors ◽

Error Covariance ◽

Convective Scale ◽

Cloud Resolving Model ◽

Comparable Accuracy

Abstract A four-dimensional variational data assimilation (4DVAR) algorithm is compared to an ensemble Kalman filter (EnKF) for the assimilation of radar data at the convective scale. Using a cloud-resolving model, simulated, imperfect radar observations of a supercell storm are assimilated under the assumption of a perfect forecast model. Overall, both assimilation schemes perform well and are able to recover the supercell with comparable accuracy, given radial-velocity and reflectivity observations where rain was present. 4DVAR produces generally better analyses than the EnKF given observations limited to a period of 10 min (or three volume scans), particularly for the wind components. In contrast, the EnKF typically produces better analyses than 4DVAR after several assimilation cycles, especially for model variables not functionally related to the observations. The advantages of the EnKF in later cycles arise at least in part from the fact that the 4DVAR scheme implemented here does not use a forecast from a previous cycle as background or evolve its error covariance. Possible reasons for the initial advantage of 4DVAR are deficiencies in the initial ensemble used by the EnKF, the temporal smoothness constraint used in 4DVAR, and nonlinearities in the evolution of forecast errors over the assimilation window.

Download Full-text

A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

Weather and Forecasting ◽

10.1175/waf-d-15-0013.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1158-1181 ◽

Cited By ~ 41

Author(s):

Craig S. Schwartz ◽

Glen S. Romine ◽

Morris L. Weisman ◽

Ryan A. Sobash ◽

Kathryn R. Fossell ◽

...

Keyword(s):

Kalman Filter ◽

Real Time ◽

Ensemble Kalman Filter ◽

Wrf Model ◽

Severe Weather ◽

Ensemble Prediction ◽

Limited Area ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

Download Full-text

Higher Resolution in an Operational Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-13-00138.1 ◽

2014 ◽

Vol 142 (3) ◽

pp. 1143-1162 ◽

Cited By ~ 71

Author(s):

P. L. Houtekamer ◽

Xingxiu Deng ◽

Herschel L. Mitchell ◽

Seung-Jong Baek ◽

Normand Gagnon

Keyword(s):

Kalman Filter ◽

Temporal Resolution ◽

Ensemble Kalman Filter ◽

Forecast Model ◽

Horizontal Resolution ◽

Ensemble Prediction ◽

Time Step ◽

Ensemble Prediction System ◽

Medium Range ◽

The Impact

Abstract Recently, the computing facilities available to the Meteorological Service of Canada were significantly upgraded. This provided an opportunity to improve the resolution of the global ensemble Kalman filter (EnKF) and the medium-range Global Ensemble Prediction System (GEPS). In the EnKF, the main upgrades include improved horizontal, vertical, and temporal resolution. With the introduction of the higher horizontal resolution, it was decided to use a filtered topography in order to address an occasional instability problem. At the same time, the number of assimilated radiance observations was increased via a relaxation of the data-thinning procedures. In the medium-range GEPS, which already used the higher horizontal resolution, the filtered topography was also adopted. Likewise, the temporal resolution was increased to be the same as in the short-range integrations of the EnKF. With these changes, the grid used by the Canadian EnKF has 600 × 300 points in the horizontal and 74 vertical levels. The forecast model uses a 20-min time step and, for time interpolation of the model trajectories, model states are stored every hour. The EnKF uses an ensemble having 192 members. This paper sequentially examines the impact of these implemented changes. The upgraded EnKF became operational at the Canadian Meteorological Centre in mid-February 2013.

Download Full-text

Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts

Weather and Forecasting ◽

10.1175/2010waf2222392.1 ◽

2010 ◽

Vol 25 (5) ◽

pp. 1568-1573 ◽

Cited By ~ 2

Author(s):

Takemasa Miyoshi ◽

Takuya Komori ◽

Hitoshi Yonehara ◽

Ryota Sakai ◽

Munenhiko Yamaguchi

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Initial Conditions ◽

Global Analysis ◽

Weather Prediction ◽

Japan Meteorological Agency ◽

Ensemble Prediction ◽

Initial Condition ◽

Ensemble Prediction System ◽

Typhoon Track

Abstract The operational numerical weather prediction (NWP) systems at the Japan Meteorological Agency (JMA) indicated that the typhoon track forecasts made by the control member of the ensemble prediction system (EPS) tended to be worse than those made by the high-resolution global NWP. The control forecast of the EPS with horizontal triangular truncation at 319 wavenumbers and 60 vertical levels (T319/L60 resolution) was initialized by eliminating the higher-wavenumber components of the global analysis at T959/L60 resolution. When the data assimilation cycle was performed at the lower T319/L60 resolution, the forecast gave typhoon track forecasts closer to the high-resolution global NWP. Therefore, it stands to reason that the resolution transform of the initial condition must be responsible for the degradation of the typhoon track forecasts at least to considerable extent. To improve the low-resolution forecast, two approaches are tested in this study: 1) applying a smoother spectral truncation for the resolution transform and 2) performing noncycled lower-resolution data assimilation during preprocessing. Results from the single case study of Typhoon Nuri (2008) indicate almost no impact from the former approach, but a significant positive impact when using the latter approach. The results of this study illuminate the importance of considering a model’s resolving capability during data assimilation. Namely, if the initial conditions contain features caused by unresolved scales, degraded forecasts may result.

Download Full-text