Can a Moderate-Resolution Limited-Area Data Assimilation System Add Value to the Global Analysis of Tropical Cyclones?

Abstract This study investigates the benefits of employing a limited-area data assimilation (DA) system to enhance lower-resolution global analyses in the northwest Pacific tropical cyclone (TC) basin. Numerical experiments are carried out with a global analysis system at horizontal resolution T62 and a limited-area analysis system at resolutions from 200 to 36 km. The global and limited-area DA systems, which are both based on the local ensemble transform Kalman filter algorithm, are implemented using a unique configuration, in which the global DA system provides information about the large-scale analysis and background uncertainty to the limited-area DA system. The limited-area analyses of the storm locations are, on average, more accurate than those from the global analyses, but increasing the resolution of the limited-area system beyond 100 km has little benefit. Two factors contribute to the higher accuracy of the limited-area analyses. First, the limited-area system improves the accuracy of the location estimates for strong storms, which is introduced when the background is updated by the global assimilation. Second, it improves the accuracy of the background estimate of the storm locations for moderate and weak storms. Improvements in the steering flow analysis as a result of increased resolution are modest and short lived in the forecasts. Limited-area track forecasts are more accurate, on average, than global forecasts, independently of the strength of the storms up to five days. This forecast improvement is due to the more accurate analysis of the initial position of storms and the better representation of the interactions between the storms and their immediate environment.

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Strategies for coupling global and limited-area ensemble Kalman filter assimilation

Nonlinear Processes in Geophysics ◽

10.5194/npg-18-415-2011 ◽

2011 ◽

Vol 18 (3) ◽

pp. 415-430 ◽

Cited By ~ 6

Author(s):

D. Merkova ◽

I. Szunyogh ◽

E. Ott

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Horizontal Resolution ◽

Wave Number ◽

Limited Area ◽

Forecast Performance ◽

Perfect Model ◽

Forecast Time ◽

Area Data ◽

Coupling Strategies

Abstract. This paper compares the forecast performance of four strategies for coupling global and limited area data assimilation: three strategies propagate information from the global to the limited area process, while the fourth strategy feeds back information from the limited area to the global process. All four strategies are formulated in the Local Ensemble Transform Kalman Filter (LETKF) framework. Numerical experiments are carried out with the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and the NCEP Regional Spectral Model (RSM). The limited area domain is an extended North-America region that includes part of the north-east Pacific. The GFS is integrated at horizontal resolution T62 (about 150 km in the mid-latitudes), while the RSM is integrated at horizontal resolution 48 km. Experiments are carried out both under the perfect model hypothesis and in a realistic setting. The coupling strategies are evaluated by comparing their deterministic forecast performance at 12-h and 48-h lead times. The results suggest that the limited area data assimilation system has the potential to enhance the forecasts at 12-h lead time in the limited area domain at the synoptic and sub-synoptic scales (in the global wave number range of about 10 to 40). There is a clear indication that between the forecast performance of the different coupling strategies those that cycle the limited area assimilation process produce the most accurate forecasts. In the realistic setting, at 12-h forecast time the limited area systems produce more modest improvements compared to the global system than under the perfect model hypothesis, and at 48-h forecast time the global forecasts are more accurate than the limited area forecasts.

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A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

Weather and Forecasting ◽

10.1175/waf-d-10-05033.1 ◽

2012 ◽

Vol 27 (1) ◽

pp. 124-140 ◽

Cited By ~ 11

Author(s):

Bin Liu ◽

Lian Xie

Keyword(s):

Data Assimilation ◽

Large Scale ◽

Dynamical Downscaling ◽

Weather Forecasting ◽

Forecast Error ◽

Regional Model ◽

Small Scale ◽

Limited Area ◽

Track Forecast ◽

Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

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AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v68.30917 ◽

2016 ◽

Vol 68 (1) ◽

pp. 30917 ◽

Cited By ~ 27

Author(s):

Chun Yang ◽

Zhiquan Liu ◽

Jamie Bresch ◽

Syed R. H. Rizvi ◽

Xiang-Yu Huang ◽

...

Keyword(s):

Data Assimilation ◽

Hurricane Sandy ◽

Limited Area ◽

Radiance Assimilation ◽

Sky Radiance ◽

Data Assimilation System ◽

Assimilation System ◽

Area Data

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Issues in high resolution limited area data assimilation for quantitative precipitation forecasting

Physica D Nonlinear Phenomena ◽

10.1016/j.physd.2004.05.001 ◽

2004 ◽

Vol 196 (1-2) ◽

pp. 1-27 ◽

Cited By ~ 19

Author(s):

S.L. Dance

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Limited Area ◽

Precipitation Forecasting ◽

Area Data

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Diagnosing Summertime Mesoscale Vertical Motion: Implications for Atmospheric Data Assimilation

Monthly Weather Review ◽

10.1175/mwr3371.1 ◽

2007 ◽

Vol 135 (6) ◽

pp. 2076-2094 ◽

Cited By ~ 16

Author(s):

Christian Pagé ◽

Luc Fillion ◽

Peter Zwack

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Control Variable ◽

Vertical Motion ◽

Forecast Model ◽

Horizontal Resolution ◽

Limited Area ◽

Significant Information ◽

Special Procedure ◽

Atmospheric Data

Abstract Balance omega equations have recently been used to try to improve the characterization of balance in variational data assimilation schemes for numerical weather prediction (NWP). Results from Fisher and Fillion et al. indicate that a quasigeostrophic omega equation can be used adequately in the definition of the control variable to represent synoptic-scale balanced vertical motion. For high-resolution limited-area data assimilation and forecasting (1–10-km horizontal resolution), such a diagnostic equation for vertical motion needs to be revisited. Using a state-of-the-art NWP forecast model at 2.5-km horizontal resolution, these issues are examined. Starting from a complete diagnostic partial differential equation for omega, the rhs forcing terms were computed from model-generated fields. These include the streamfunction, temperature, and physical time tendencies of temperature in gridpoint space. To accurately compute one term of second-order importance (i.e., the ageostrophic vorticity tendency forcing term), a special procedure was used. With this procedure it is shown that Charney’s balance equation brings significant information in order to deduce the geostrophic time tendency term. Under these conditions, results show that for phenomena of length scales of 15–100 km over convective regions, a diagnostic equation can capture the major part of the model-generated vertical motion. The limitations of the digital filter initialization approach when used as in Fillion et al. with a cutoff period reduced to 1 h are also illustrated. The potential usefulness of this study for mesoscale atmospheric data assimilation is briefly discussed.

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Assimilation of Tropical Cyclone Observations: Improving the Assimilation of TCVitals, Scatterometer Winds, and Dropwindsonde Observations

Monthly Weather Review ◽

10.1175/mwr-d-14-00158.1 ◽

2015 ◽

Vol 143 (10) ◽

pp. 3956-3980 ◽

Cited By ~ 4

Author(s):

Christina Holt ◽

Istvan Szunyogh ◽

Gyorgyi Gyarmati ◽

S. Mark Leidner ◽

Ross N. Hoffman

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Statistical Model ◽

Global Analysis ◽

Second Order ◽

Statistical Moments ◽

Limited Area ◽

Data Assimilation Scheme ◽

Assimilation Scheme ◽

Normally Distributed

Abstract The standard statistical model of data assimilation assumes that the background and observation errors are normally distributed, and the first- and second-order statistical moments of the two distributions are known or can be accurately estimated. Because these assumptions are never satisfied completely in practice, data assimilation schemes must be robust to errors in the underlying statistical model. This paper tests simple approaches to improving the robustness of data assimilation in tropical cyclone (TC) regions. Analysis–forecast experiments are carried out with three types of data—Tropical Cyclone Vitals (TCVitals), DOTSTAR, and QuikSCAT—that are particularly relevant for TCs and with an ensemble-based data assimilation scheme that prepares a global analysis and a limited-area analysis in a TC basin simultaneously. The results of the experiments demonstrate that significant analysis and forecast improvements can be achieved for TCs that are category 1 and higher by improving the robustness of the data assimilation scheme.

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Spectral nudging in an hourly 4DVar framework: Status and Plans

10.5194/egusphere-egu21-6618 ◽

2021 ◽

Author(s):

Marco Milan ◽

Adam Clayton ◽

Andrew Lorenc ◽

Gareth Dow ◽

Roberts Tubbs ◽

...

Keyword(s):

Large Scale ◽

Global Analysis ◽

Global Model ◽

Limited Area ◽

Spectral Nudging ◽

Convective Systems ◽

Convective Scale ◽

Scale Error ◽

Set Up ◽

Background Fields

The Met Office hourly 4D-Var was introduced operationally to its convective-scale limited area model (UKV) in summer 2017, improving forecast skill for nowcasting and short-range purposes. However, in recent tests a downscaler run from a global analysis tends to be better than hourly 4D-Var, especially for some variables (e.g. screen temperature). This is probably due to a poor representation of large-scale dynamics in the LAM DA system, which is now integrated on an extended domain, whilst the global model has improved to a 10km resolution and with better DA (hybrid 4D-Var). Therefore, the MO recognises the necessity of coupling large scale dynamics with convective systems using the better estimation of these motions from the global model. We opted for a solution similar to spectral nudging, which uses large scale increments derived from a model with a better representation of these scales. At the same time, the short scales from UKV are maintained. We call this method &#8216;Background Increments&#8217; (BGInc), as it updates the UKV background fields using a spectrally filtered increment derived from a different (global) model. This update is calculated just prior to computing the analysis increments from the hourly DA cycle. We investigated different set-ups for the implementation, changing the cut-off wavelength, the vertical weights, the frequency of updates of BGInc and other set-up features. This novel system is now in a testing phase for operational purposes. From preliminary results, the forecast is improved for about the first 12 hours for different variables. We also notice a reduction in the gravity wave activity generated when new lateral boundary conditions are introduced to the LAM from the latest global forecast. This research shows the benefits of a better representation of large-scale motions for LAM forecasts. In the short term, future development involves the computation of new static covariances using a better representation of the large-scale error. In the longer term, this technique could be useful in a hybrid 4D-Var scheme while enabling the use of large-scale ensemble perturbations in the analysis without causing large adjustments at the lateral boundaries.

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Impact of Grid Nudging Parameters on Dynamical Downscaling during Summer over Chinese Mainland

10.20944/preprints201709.0064.v1 ◽

2017 ◽

Author(s):

Xiaoping Mai ◽

Yuanyuan Ma ◽

Yi Yang ◽

Deqin Li ◽

Xiaobin Qiu

Keyword(s):

Water Vapor ◽

Large Scale ◽

Dynamical Downscaling ◽

Global Analysis ◽

Regional Climate ◽

Chinese Mainland ◽

Horizontal Resolution ◽

Horizontal Wind ◽

Environmental Prediction ◽

Almost All

The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable had a larger impact on the downscaling results and (2) what was the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the Chinese mainland. To solve this queries, 41 3-month long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements; nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. Moreover, the optimum nudging scheme varied with simulated regions and layers. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable.

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