scholarly journals Assessing the Performance of an Ensemble Forecast System in Predicting the Magnitude and the Spectrum of Analysis and Forecast Uncertainties

2011 ◽  
Vol 139 (4) ◽  
pp. 1207-1223 ◽  
Author(s):  
Elizabeth Satterfield ◽  
Istvan Szunyogh
Keyword(s):  
Ensemble Spread ◽  
Forecast System ◽  
Forecast Uncertainty ◽  
Total Magnitude ◽  
Real Atmosphere ◽  
Grid Points ◽  
Ensemble Transform Kalman Filter ◽  
Environmental Prediction ◽  
The Impact ◽  
Data Assimilation System

The ability of an ensemble to capture the magnitude and spectrum of uncertainty in a local linear space spanned by the ensemble perturbations is assessed. Numerical experiments are carried out with a reduced resolution 2004 version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The local ensemble transform Kalman filter (LETKF) data assimilation system is used to assimilate observations in three steps, gradually adding more realistic features to the observing network. In the first experiment, randomly placed, noisy, simulated vertical soundings, which provide 10% coverage of horizontal model grid points, are assimilated. Next, the impact of an inhomogeneous observing system is introduced by assimilating simulated observations in the locations of real observations of the atmosphere. Finally, observations of the real atmosphere are assimilated. The most important findings of this study are the following: predicting the magnitude of the forecast uncertainty and the relative importance of the different patterns of uncertainty is, in general, a more difficult task than predicting the patterns of uncertainty; the ensemble, which is tuned to provide near-optimal performance at analysis time, underestimates not only the total magnitude of the uncertainty, but also the magnitude of the uncertainty that projects onto the space spanned by the ensemble perturbations; and finally, a strong predictive linear relationship is found between the local ensemble spread and the upper bound of the local forecast uncertainty.

scholarly journals The Impact of Air–Sea Interactions on the Simulation of Tropical Intraseasonal Variability

2008 ◽  
Vol 21 (24) ◽  
pp. 6616-6635 ◽  
Author(s):  
Kathy Pegion ◽  
Ben P. Kirtman
Keyword(s):  
Intraseasonal Variability ◽  
Intraseasonal Oscillation ◽  
Coupled Model ◽  
Coupled Simulation ◽  
Maritime Continent ◽  
Forecast System ◽  
Climate Forecast System ◽  
Environmental Prediction ◽  
The Impact ◽  
The Western Pacific

Abstract The impact of coupled air–sea feedbacks on the simulation of tropical intraseasonal variability is investigated in this study using the National Centers for Environmental Prediction Climate Forecast System. The simulation of tropical intraseasonal variability in a freely coupled simulation is compared with two simulations of the atmospheric component of the model. In one experiment, the uncoupled model is forced with the daily sea surface temperature (SST) from the coupled run. In the other, the uncoupled model is forced with climatological SST from the coupled run. Results indicate that the overall intraseasonal variability of precipitation is reduced in the coupled simulation compared to the uncoupled simulation forced by daily SST. Additionally, air–sea coupling is responsible for differences in the simulation of the tropical intraseasonal oscillation between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The differences between the coupled and uncoupled simulations are due to the fact that the relationships between precipitation and SST and latent heat flux and SST are much stronger in the coupled model than in the uncoupled model. Additionally, these relationships are delayed by about 5 days in the uncoupled model compared to the coupled model. As demonstrated by the uncoupled simulation forced with climatological SST, some of the intraseasonal oscillation can be simulated by internal atmospheric dynamics. However, the intraseasonally varying SST appears to be important to the amplitude and propagation of the oscillation beyond the Maritime Continent.

scholarly journals Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

2016 ◽  
Vol 113 (42) ◽  
pp. 11765-11769 ◽  
Author(s):  
Banglin Zhang ◽  
Richard S. Lindzen ◽  
Vijay Tallapragada ◽  
Fuzhong Weng ◽  
Qingfu Liu ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
Forecast Model ◽  
Vertical Resolution ◽  
Global Forecast System ◽  
Forecast System ◽  
Atlantic Basin ◽  
Hurricane Track ◽  
Environmental Prediction ◽  
The Impact ◽  
Positive Impacts

The atmosphere−ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels.

scholarly journals A Comparison of the Impacts of Radiosonde and AMSU Radiance Observations in GSI Based 3DEnsVar and 3DVar Data Assimilation Systems for NCEP GFS

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Govindan Kutty ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Winter Season ◽  
Data Quality Control ◽  
Hybrid Data Assimilation ◽  
Environmental Prediction ◽  
Hemisphere Winter ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

The impact of observations can be dependent on many factors in a data assimilation (DA) system including data quality control, preprocessing, skill of the model, and the DA algorithm. The present study focuses on comparing the impacts of observations assimilated by two different DA algorithms. A three-dimensional ensemble-variational (3DEnsVar) hybrid data assimilation system was recently developed based on the Gridpoint Statistical Interpolation (GSI) data assimilation system and was implemented operationally for the National Center for Environmental Prediction (NCEP) Global Forecast System (GFS). One question to address is, how the impacts of observations on GFS forecasts differ when assimilated by the traditional GSI-three dimensional variational (3DVar) and the new 3DEnsVar. Experiments were conducted over a 6-week period during Northern Hemisphere winter season at a reduced resolution. For both the control and data denial experiments, the forecasts produced by 3DEnsVar were more accurate than GSI3DVar experiments. The results suggested that the observations were better and more effectively exploited to increment the background forecast in 3DEnsVar. On the other hand, in GSI3DVar, where the observation will be making mostly local, isotropic increments without proper flow dependent extrapolation is more sensitive to the number and types observations assimilated.

A Simulation Study Using a Local Ensemble Transform Kalman Filter for Data Assimilation in New York Harbor

2008 ◽  
Vol 25 (9) ◽  
pp. 1638-1656 ◽  
Author(s):  
Ross N. Hoffman ◽  
Rui M. Ponte ◽  
Eric J. Kostelich ◽  
Alan Blumberg ◽  
Istvan Szunyogh ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Data Assimilation ◽  
External Forcing ◽  
Ensemble Spread ◽  
Initial Ensemble ◽  
Error Standard Deviation ◽  
Grid Points ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform

Abstract Data assimilation approaches that use ensembles to approximate a Kalman filter have many potential advantages for oceanographic applications. To explore the extent to which this holds, the Estuarine and Coastal Ocean Model (ECOM) is coupled with a modern data assimilation method based on the local ensemble transform Kalman filter (LETKF), and a series of simulation experiments is conducted. In these experiments, a long ECOM “nature” run is taken to be the “truth.” Observations are generated at analysis times by perturbing the nature run at randomly chosen model grid points with errors of known statistics. A diverse collection of model states is used for the initial ensemble. All experiments use the same lateral boundary conditions and external forcing fields as in the nature run. In the data assimilation, the analysis step combines the observations and the ECOM forecasts using the Kalman filter equations. As a control, a free-running forecast (FRF) is made from the initial ensemble mean to check the relative importance of external forcing versus data assimilation on the analysis skill. Results of the assimilation cycle and the FRF are compared to truth to quantify the skill of each. The LETKF performs well for the cases studied here. After just a few assimilation cycles, the analysis errors are smaller than the observation errors and are much smaller than the errors in the FRF. The assimilation quickly eliminates the domain-averaged bias of the initial ensemble. The filter accurately tracks the truth at all data densities examined, from observations at 50% of the model grid points down to 2% of the model grid points. As the data density increases, the ensemble spread, bias, and error standard deviation decrease. As the ensemble size increases, the ensemble spread increases and the error standard deviation decreases. Increases in the size of the observation error lead to a larger ensemble spread but have a small impact on the analysis accuracy.

scholarly journals Predictability of the Performance of an Ensemble Forecast System: Predictability of the Space of Uncertainties

2010 ◽  
Vol 138 (3) ◽  
pp. 962-981 ◽  
Author(s):  
Elizabeth Satterfield ◽  
Istvan Szunyogh
Keyword(s):  
Linear Space ◽  
Linear Representation ◽  
Ensemble Prediction ◽  
Local Error ◽  
Forecast System ◽  
Ensemble Prediction System ◽  
Ensemble Performance ◽  
Ensemble Transform Kalman Filter ◽  
Environmental Prediction ◽  
Forecast Time

Abstract The performance of an ensemble prediction system is inherently flow dependent. This paper investigates the flow dependence of the ensemble performance with the help of linear diagnostics applied to the ensemble perturbations in a small local neighborhood of each model gridpoint location ℓ. A local error covariance matrix 𝗣ℓ is defined for each local region, and the diagnostics are applied to the linear space defined by the range of the ensemble-based estimate of 𝗣ℓ. The particular diagnostics are chosen to help investigate the efficiency of in capturing the space of analysis and forecast uncertainties. Numerical experiments are carried out with an implementation of the local ensemble transform Kalman filter (LETKF) data assimilation system on a reduced-resolution [T62 and 28 vertical levels (T62L28)] version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Both simulated observations under the perfect model scenario and observations of the real atmosphere in a realistic setting are used in these experiments. It is found that (i) paradoxically, the linear space provides an increasingly better estimate of the space of forecast uncertainties as the time evolution of the ensemble perturbations becomes more nonlinear with increasing forecast time; (ii) provides a more reliable linear representation of the space of forecast uncertainties for cases of more rapid error growth (i.e., for cases of lower predictability); and (iii) the ensemble dimension (E dimension) is a reliable predictor of the performance of in predicting the space of forecast uncertainties.

Accounting for model error in atmospheric forecasts

2020 ◽  
Author(s):  
William Crawford ◽  
Sergey Frolov ◽  
Justin McLay ◽  
Carolyn Reynolds ◽  
Craig Bishop ◽  
...  
Keyword(s):  
Positive Impact ◽  
Model Error ◽  
Initial Time ◽  
Deterministic System ◽  
Ensemble Spread ◽  
Transform Method ◽  
Subsequent Cycle ◽  
Ensemble Systems ◽  
The Impact ◽  
Data Assimilation System

<p>The presented work will illustrate the impact of analysis correction based additive inflation (ACAI) on atmospheric forecasts. ACAI uses analysis corrections from the NAVGEM data assimilation system as a representation of model error and is shown to simultaneously improve ensemble spread-skill, reduce model bias and improve the RMS error in the ensemble mean. Results are presented from a myriad of experiments exercising ACAI in stand-alone NAVGEM forecasts using two different ensemble systems; (1) the current operational EPS at FNMOC based on the ensemble transform method and (2) the Navy-ESPC EPS based on perturbed observations. The method of relaxation-to-prior-perturbations (RTPP) has also been implemented in the Navy-ESPC EPS and is shown to further improve the ensemble spread-skill relationship by allowing variance generated during the forecast to impact the initial-time ensemble variance in the subsequent cycle. Results from a simplified implementation of ACAI in the NAVGEM deterministic system will also be shown and indicate positive impact to model biases and RMSE.</p>

scholarly journals An Observing System Experiment for Tropical Cyclone Targeting Techniques Using the Global Forecast System

2011 ◽  
Vol 139 (3) ◽  
pp. 895-907 ◽  
Author(s):  
Sim D. Aberson ◽  
Sharanya J. Majumdar ◽  
Carolyn A. Reynolds ◽  
Brian J. Etherton
Keyword(s):  
Tropical Cyclone ◽  
Total Energy ◽  
Numerical Models ◽  
Virgin Islands ◽  
Singular Vectors ◽  
Research Division ◽  
System Experiment ◽  
Ensemble Transform Kalman Filter ◽  
Environmental Prediction ◽  
The Impact

Abstract In 1997, the National Oceanic and Atmospheric Administration’s National Hurricane Center and the Hurricane Research Division began operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve the numerical guidance for hurricanes that threaten the continental United States, Puerto Rico, the U.S. Virgin Islands, and Hawaii. The dropwindsonde observations from these missions were processed and formatted aboard the aircraft and sent to the National Centers for Environmental Prediction and the Global Telecommunications System to be ingested into the Global Forecasting System, which serves as initial and boundary conditions for regional numerical models that also forecast tropical cyclone track and intensity. As a result of limited aircraft resources, optimal observing strategies for these missions are investigated. An Observing System Experiment in which different configurations of the dropwindsonde data based on three targeting techniques (ensemble variance, ensemble transform Kalman filter, and total energy singular vectors) are assimilated into the model system was conducted. All three techniques show some promise in obtaining maximal forecast improvements while limiting flight time and expendables. The data taken within and around the regions specified by the total energy singular vectors provide the largest forecast improvements, though the sample size is too small to make any operational recommendations. Case studies show that the impact of dropwindsonde data obtained either outside of fully sampled, or within nonfully sampled target regions is generally, though not always, small; this suggests that the techniques are able to discern in which regions extra observations will impact the particular forecast.

scholarly journals A Two-Season Impact Study of NOAA Polar-Orbiting Satellites in the NCEP Global Data Assimilation System

2008 ◽  
Vol 23 (5) ◽  
pp. 854-877 ◽  
Author(s):  
James A. Jung ◽  
Tom H. Zapotocny ◽  
John F. Le Marshall ◽  
Russ E. Treadon
Keyword(s):  
Data Assimilation ◽  
Forecast System ◽  
Anomaly Correlation ◽  
Global Data Assimilation System ◽  
Polar Orbiting Satellite ◽  
The Impact ◽  
Data Assimilation System ◽  
Global Data ◽  
Assimilation System ◽  
Forecast Quality

Abstract Observing system experiments (OSEs) during two seasons are used to quantify the important contributions made to forecast quality from the use of the National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting satellites. The impact is measured by comparing the analysis and forecast results from an assimilation–forecast system using one NOAA polar-orbiting satellite with results from using two and three polar-orbiting satellites in complementary orbits. The assimilation–forecast system used for these experiments is the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System–Global Forecast System (GDAS–GFS). The case studies chosen consist of periods during January–February and August–September 2003. Differences between the forecasts are accumulated over the two seasons and are analyzed to demonstrate the impact of these satellites. Anomaly correlations (ACs) and geographical forecasts (FIs) are evaluated for all experimental runs during both seasons. The anomaly correlations are generated using the standard NCEP verification software suite and cover the polar regions (60°–90°) and midlatitudes (20°–80°) of each hemisphere. The rms error for 850- and 200-hPa wind vector differences are shown for the tropical region (20°N–20°S). The geographical distribution of forecast impact on geopotential heights, relative humidity, precipitable water, and the u component of wind are also examined. The results demonstrate that the successive addition of each NOAA polar-orbiting satellite increases forecast quality. The use of three NOAA polar-orbiting satellites generally provides the largest improvement to the anomaly correlation scores in the polar and midlatitude regions. Improvements to the anomaly correlation scores are also realized from the use of two NOAA polar-orbiting satellites over only one. The forecast improvements from two satellites are generally smaller than if using three satellites, consistent with the increase in areal coverage obtained with the third satellite.

scholarly journals The Spread of Tropical Storm Tracks in Three Versions of NCEP’s Global Ensemble Model: Focus on Hurricane Edouard (2014)

2019 ◽  
Vol 34 (3) ◽  
pp. 577-586
Author(s):  
Frank P. Colby
Keyword(s):  
Data Assimilation ◽  
Storm Track ◽  
Ensemble Member ◽  
Ensemble Model ◽  
Detailed Data ◽  
Forecast System ◽  
The Family ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Since 2012, the National Centers for Environmental Prediction’s Global Ensemble Forecast System (GEFS) has undergone two major upgrades. Version 11 was introduced in December 2015, with a new dynamic scheme, improved physics, increased horizontal and vertical resolution, and a more accurate initialization method. Prior to implementation, retrospective model runs over four years were made, covering multiple hurricane seasons. The second major upgrade was implemented in May 2016, when the data assimilation system for the deterministic Global Forecast System (GFS) was upgraded. Because the GEFS initialization is taken from the deterministic GFS, this upgrade had a direct impact on the GEFS. Unlike the previous upgrade, the model was rerun for only a few tropical cyclones. Hurricane Edouard (2014) was the storm for which the most retrospective runs (4) were made for the new data assimilation system. In this paper, the impact of the GEFS upgrades is examined using seasonal data for the 2014–17 hurricane seasons, and detailed data from the four model runs made for Hurricane Edouard. Both upgrades reduced the spread between ensemble member tracks. The first upgrade reduced the spread but did not reduce the likelihood that the actual track would be included in the family of member tracks. The second upgrade both reduced the spread further and reduced the chance that the real storm track would be within the envelope of member tracks.

The HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for High-Resolution Data: The Impact of Airborne Doppler Radar Observations in an OSSE

2012 ◽  
Vol 140 (6) ◽  
pp. 1843-1862 ◽  
Author(s):  
Altuğ Aksoy ◽  
Sylvie Lorsolo ◽  
Tomislava Vukicevic ◽  
Kathryn J. Sellwood ◽  
Sim D. Aberson ◽  
...  
Keyword(s):  
High Resolution ◽  
Doppler Radar ◽  
Model Error ◽  
Ensemble Spread ◽  
Error Statistics ◽  
Ensemble Data ◽  
The Impact ◽  
Airborne Doppler Radar ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Within the National Oceanic and Atmospheric Administration, the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory has developed the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) to assimilate hurricane inner-core observations for high-resolution vortex initialization. HEDAS is based on a serial implementation of the square root ensemble Kalman filter. HWRF is configured with a horizontal grid spacing of km on the outer/inner domains. In this preliminary study, airborne Doppler radar radial wind observations are simulated from a higher-resolution km version of the same model with other modifications that resulted in appreciable model error. A 24-h nature run simulation of Hurricane Paloma was initialized at 1200 UTC 7 November 2008 and produced a realistic, category-2-strength hurricane vortex. The impact of assimilating Doppler wind observations is assessed in observation space as well as in model space. It is observed that while the assimilation of Doppler wind observations results in significant improvements in the overall vortex structure, a general bias in the average error statistics persists because of the underestimation of overall intensity. A general deficiency in ensemble spread is also evident. While covariance inflation/relaxation and observation thinning result in improved ensemble spread, these do not translate into improvements in overall error statistics. These results strongly suggest a need to include in the ensemble a representation of forecast error growth from other sources such as model error.

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