The Impact of Assimilating Dropwindsonde Data Deployed at Different Sites on Typhoon Track Forecasts

2013 ◽  
Vol 141 (8) ◽  
pp. 2669-2682 ◽  
Author(s):  
Boyu Chen ◽  
Mu Mu ◽  
Qin Xiaohao
Keyword(s):  
Mesoscale Model ◽  
Three Dimensional ◽  
Weather Research And Forecasting ◽  
Surveillance Program ◽  
State University ◽  
Optimal Perturbation ◽  
Fifth Generation ◽  
Typhoon Track ◽  
Adaptive Observation ◽  
The Impact

Abstract This study investigates the impacts on typhoon track forecasting by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and its three-dimensional variational data assimilation (3DVAR) system of assimilating dropwindsonde observational data acquired from different sites. All of the sonde data were obtained between 2004 and 2009 in the typhoon surveillance program Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). Experiments were conducted to test the model's response to five scenarios involving differing dropwindsonde data inputs: 1) no dropwindsonde data, 2) all available dropwindsonde data, 3) data gathered in sensitive regions identified by the conditional nonlinear optimal perturbation (CNOP) approach, 4) data gathered in sensitive regions identified by the first singular vector (FSV) approach, and 5) several sondes selected at random. The results show that using dropwindsonde data based on CNOP sensitivity can lead to improvements in typhoon track forecasting similar to, and occasionally better than, those achieved by assimilating all of the available data. Both approaches offered greater benefits than the other three alternatives averagely. It is proposed that CNOP provides a suitable approach to determining sensitive regions during adaptive observation of typhoons. Similar results may be obtained if the sensitivity products developed using MM5 are employed in the Weather Research and Forecasting Model (WRF), suggesting that it is applicable to utilize sensitivity produced by MM5 in WRF.

Two-Dimensional Idealized Simulations of the Impact of Multiple Windward Ridges on Orographic Precipitation

2008 ◽  
Vol 65 (2) ◽  
pp. 509-523 ◽  
Author(s):  
Brian A. Colle
Keyword(s):  
Mesoscale Model ◽  
Two Dimensional ◽  
State University ◽  
Mountain Waves ◽  
Freezing Level ◽  
Fifth Generation ◽  
Warm Rain ◽  
Windward Slope ◽  
The Impact ◽  
Precipitation Enhancement

Abstract This paper presents two-dimensional (2D) idealized simulations at 1-km grid spacing using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in order to illustrate how a series of ridges along a broad windward slope can impact the precipitation distribution and simulated microphysics. The number of windward ridges for a 2000-m mountain of 50-km half-width is varied from 0 to 16 over a 150-km distance using different stratifications, freezing levels, uniform ambient flows, and ridge amplitudes. A few (200–400 m) windward ridges can enhance the precipitation locally over each ridge crest by a factor of 2–3. Meanwhile, a series of 8–16 ridges that are 200–400 m in height can increase the net precipitation averaged over the windward slope by 10%–35%. This average precipitation enhancement is maximized when the ridge spacing is relatively small (<20 km), since there is less time for subsidence drying within the valleys and the mountain waves become more evanescent, which favors a simple upward and downward motion couplet over each ridge. In addition, small ridge spacing is shown to have a synergistic effect on precipitation over the lower windward slope, in which an upstream ridge helps increase the precipitation over the adjacent downwind ridge. There is little net precipitation enhancement by the ridges for small moist Froude numbers (Fr < 0.8), since flow blocking limits the flow up and over each ridge. For a series of narrow ridges (∼10 km wide), the largest precipitation enhancement for a 500-mb freezing level occurs over lower windward slope of the barrier through warm-rain processes. In contrast, a 1000-mb freezing level has the largest precipitation enhancement over the middle and upper portions of a barrier for a series of narrow (∼10 km wide) ridges given the horizontal advection of snow aloft.

scholarly journals Digital Filter Initialization for MM5

2006 ◽  
Vol 134 (4) ◽  
pp. 1222-1236 ◽  
Author(s):  
Min Chen ◽  
Xiang-Yu Huang
Keyword(s):  
Digital Filter ◽  
Mesoscale Model ◽  
Heavy Rain ◽  
State University ◽  
Initial Model ◽  
Pennsylvania State University ◽  
Fifth Generation ◽  
Beijing Institute ◽  
The Impact

Abstract In this paper several configurations of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5), which is implemented at Beijing Institute of Urban Meteorology in China, are used to demonstrate the initial noise problem caused either by interpolating global model fields onto an MM5 grid or by using MM5 objective analysis schemes. An implementation of a digital filter initialization (DFI) package to MM5 is then documented. A heavy rain case study and intermittent data assimilation experiments are used to assess the impact of DFI on MM5 forecasts. It is shown that DFI effectively filters out the noise and produces a balanced initial model state. It is also shown that DFI improves the spinup aspects for precipitation, leading to better scores for short-range precipitation forecasts. The issues related to the initialization of variables that are not observed and/or analyzed, in particular those for nonhydrostatic quantities, are discussed.

scholarly journals A DRP–4DVar Data Assimilation Scheme for Typhoon Initialization Using Sea Level Pressure Data

2012 ◽  
Vol 140 (4) ◽  
pp. 1191-1203 ◽  
Author(s):  
Ying Zhao ◽  
Bin Wang ◽  
Juanjuan Liu
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Mesoscale Model ◽  
Storm Track ◽  
Initial Step ◽  
State University ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Fifth Generation ◽  
Typhoon Track

In this study, a new data assimilation system based on a dimension-reduced projection (DRP) technique was developed for the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) modeling system. As an initial step to test the newly developed system, observing system simulation experiments (OSSEs) were conducted using a simulated sea level pressure (SLP) field as “observations” and assimilation experiments using a specified SLP field to evaluate the effects of the new DRP–four-dimensional variational data assimilation (4DVar) method, initialization, and simulation of a tropical storm—Typhoon Bilis (2006) over the western North Pacific. In the OSSEs, the “nature” run, which was assumed to represent the “true” atmosphere, was simulated by the MM5 model, which was initialized with the 1.0° × 1.0° NCEP final global tropospheric analyses and integrated for 120 h. The simulated SLP field was then used as the observations in the data assimilation. It is shown that the MM5 DRP–4DVar system can successfully assimilate the (simulated) model output (used as observations) because the OSSEs resulted in improved storm-track forecasts. In addition, compared with an experiment that assimilated the SLP data fixed at the end of a 6-h assimilation window, the experiment that assimilated the SLP data every 3 min in a 30-min assimilation window further improved the typhoon-track forecasts, especially in terms of the initial vortex location and landfall location. Finally, the assimilation experiments with a specified SLP field have demonstrated the effectiveness of the new method.

scholarly journals A Modified Approach to Initialize an Idealized Extratropical Cyclone within a Mesoscale Model

2007 ◽  
Vol 135 (4) ◽  
pp. 1614-1624 ◽  
Author(s):  
Joseph B. Olson ◽  
Brian A. Colle
Keyword(s):  
Mesoscale Model ◽  
Weather Research And Forecasting ◽  
Extratropical Cyclones ◽  
State University ◽  
Pennsylvania State University ◽  
Short Term ◽  
Fifth Generation ◽  
Domain Boundaries ◽  
Basic States ◽  
Shear Function

Abstract A technique for initializing realistic idealized extratropical cyclones for short-term (0–72 h) numerical simulations is described. The approach modifies select methods from two previous studies to provide more control over the initial cyclone structure. Additional features added to the technique include 1) deformation functions to initialize more realistic low-level fronts, tropopause structure, and enhanced jet maximum at upper levels; 2) a barotropic shear function to help develop different cyclone and frontal geometries; and 3) damping functions to create an isolated baroclinic wave in the horizontal; therefore, the initialized cyclone is not influenced by the domain boundaries for relatively short simulations. Since this procedure allows for control of the initialized cyclone structures, it may be useful for studies of frontal and cyclone interaction with topography and mesoscale predictability. The initialization system produces a variety of basic states and synoptic disturbances, ranging from weak to explosively developing cyclones. Examples are shown to provide some insight on how to adjust selected parameters. The output is compatible with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model and the Weather Research and Forecasting model. This note describes the procedure as well as presents an example of a landfalling cyclone along the U.S. west coast with and without terrain.

scholarly journals Evaluation of Interregional Transport Using the MM5–SCIPUFF System

2004 ◽  
Vol 43 (12) ◽  
pp. 1864-1886 ◽  
Author(s):  
Aijun Deng ◽  
Nelson L. Seaman ◽  
Glenn K. Hunter ◽  
David R. Stauffer
Keyword(s):  
Mesoscale Model ◽  
Numerical Models ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Tracer Experiment ◽  
Plume Dispersion ◽  
State University ◽  
Wind Fields ◽  
The Impact ◽  
Horizontal Grid

Abstract Improved understanding of transport issues and source–receptor relationships on the interregional scale is dependent on reducing the uncertainties in the ability to define complex three-dimensional wind fields evolving in time. The numerical models used for this purpose have been upgraded substantially in recent years by introducing finer grid resolution, better representation of subgrid-scale physics, and practical four-dimensional data assimilation (FDDA) techniques that reduce the accumulation of errors over time. The impact of these improvements for interregional transport is investigated in this paper using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) and the Second-Order Closure Integrated Puff (SCIPUFF) dispersion model to simulate the 1983 Cross-Appalachian Tracer Experiment (CAPTEX-83) episode 1 of 18–19 September 1983. Combining MM5 and SCIPUFF makes it possible to verify predicted tracer concentrations against observed surface concentrations collected during the CAPTEX-83 study. Conclusions from this study are as follows. 1) Not surprisingly, a baseline model configuration reflecting typical capabilities of the late 1980s (70-km horizontal grid, 15 vertical layers, older subgrid physics, and no FDDA) produced large meteorological errors that severely degraded the accuracy of the surface tracer concentrations predicted by SCIPUFF. 2) Improving the horizontal and vertical resolution of the MM5 to 12 km (typical for current operational model) and 32 layers led to some improvements in the statistical skill, but the further addition of more advanced physics produced much greater reductions of simulation errors. 3) The use of FDDA, along with 12-km resolution and improved physics, produced the overall best performance. 4) Further reduction of the horizontal grid size to 4 km had a detrimental effect on meteorological and plume-dispersion solutions in this case because of misrepresentation of convection associated with a cold front by the MM5's explicit moist physics.

scholarly journals A Comparison of Two Tropical Cyclone Bogussing Schemes

2008 ◽  
Vol 23 (1) ◽  
pp. 194-204 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Ying Zhao ◽  
Bin Wang
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Vortex Structure ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Negative Impact ◽  
Initial Structure ◽  
State University ◽  
Fifth Generation ◽  
The Impact

Abstract The impact of two bogussing schemes on tropical cyclone (TC) forecasts is compared. One scheme for bogussing TCs into the initial conditions of the nonhydrostatic version of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is proposed by NCAR and the Air Force Weather Agency (AFWA), and four-dimensional variational data assimilation technology is employed for the other bogus data assimilation (BDA) scheme. The initial vortex structure adjusted by the NCAR–AFWA (N–A) scheme is more physically realistic, while the BDA scheme produces an initial vortex structure that is more consistent with the model. The results from 41 forecasts of TCs occurring over the western North Pacific (WNP) in 2002 suggest that the adjustment of the initial structure in the BDA scheme produces a greater benefit to the subsequent track and intensity forecasts, and the improvements in the track and intensity forecasts are significant using the BDA scheme. It seems that when using a model with 45-km grid length, the N–A scheme has a negative impact on the track forecasts for the recurving TCs and on the intensity predictions after 24 h.

scholarly journals Anthropogenic Moisture Effects on WRF Summertime Surface Temperature and Mixing Ratio Forecast Skill In Southern California

2010 ◽  
Vol 25 (5) ◽  
pp. 1522-1535 ◽  
Author(s):  
Rich F. Coleman ◽  
James F. Drake ◽  
Michael D. McAtee ◽  
Leslie O. Belsma
Keyword(s):  
Los Angeles ◽  
Mesoscale Model ◽  
Forecast Skill ◽  
Weather Research And Forecasting ◽  
State University ◽  
Los Angeles Basin ◽  
Domestic Water ◽  
Temperature Bias ◽  
Fifth Generation ◽  
Moisture Effects

Abstract Mesoscale forecasts for the Los Angeles basin made with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) exhibited a moderate to substantial warm temperature bias for extended periods in the summer months. A similar bias also was thought to exist in forecasts made using version 2.2 of the Weather Research and Forecasting Model (WRF v2.2). To address these biases, two sources of anthropogenic moisture were analyzed: commercial irrigation and outdoor domestic water use. These represent substantial amounts of equivalent precipitation that are not accounted for in normal WRF execution. This is especially true for the summer months when little or no precipitation occurs in the area. A method for estimating the temporal and spatial distributions of these two sources was developed and the resulting database was applied to model runs. The addition of these anthropogenic moisture sources is an important source of enthalpy, which results in significant cooling in WRF. However, in the course of the analysis it was determined that the biases in WRF were much smaller than had been thought. Also, despite producing significant cooling, the addition of anthropogenic moisture made only modest improvements in forecast skill, and only for some hours of the day, indicating that more research is necessary on how the physical processes are handled in WRF, and how the anthropogenic moisture is distributed during the forecast period.

scholarly journals Incremental Analysis Updates Initialization Technique Applied to 10-km MM5 and MM5 3DVAR

2006 ◽  
Vol 134 (5) ◽  
pp. 1389-1404 ◽  
Author(s):  
Mi-Seon Lee ◽  
Ying-Hwa Kuo ◽  
Dale M. Barker ◽  
Eunha Lim
Keyword(s):  
Mesoscale Model ◽  
Three Dimensional ◽  
State University ◽  
Incremental Analysis ◽  
Pennsylvania State University ◽  
High Frequencies ◽  
Fifth Generation ◽  
Cycling System ◽  
The Waves ◽  
Dynamic Variables

Abstract An incremental analysis updates (IAU) technique is implemented for 3-h updates of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) and model system with a 10-km resolution to remove spurious gravity waves. By gradually incorporating analysis increments, IAU affects only the removal of high frequencies, leaving the waves related to diurnal processes. IAU appears to be efficient in reducing the moisture spinup problem in the MM5 3DVAR cycling system. The advantage of the IAU is the most significant in improving precipitation forecasts. Rapid update cycle (RUC) with 1- and 2-h intervals in conjunction with the IAU indicates a rapid minimization and less spinup and -down problems because of greater balancing between the moisture and dynamic variables. Impact studies are performed on a heavy rainfall case that occurred in the Korean Peninsula. Verification results with a 3-h cycling system are presented on operational environments.

scholarly journals Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5

2012 ◽  
Vol 27 (2) ◽  
pp. 438-450 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Statistical Models ◽  
Mesoscale Model ◽  
Gaussian Kernel ◽  
Support Vector ◽  
State University ◽  
Rainfall Prediction ◽  
Fifth Generation ◽  
Shihmen Reservoir ◽  
Reservoir Watershed ◽  
Lag Times

Abstract This study presents two support vector machine (SVM) based models for forecasting hourly precipitation during tropical cyclone (typhoon) events. The two SVM-based models are the traditional Gaussian kernel SVMs (GSVMs) and the advanced wavelet kernel SVMs (WSVMs). A comparison between the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and statistical models, including SVM-based models and linear regressions (regression), was made in terms of performance of rainfall prediction at the Shihmen Reservoir watershed in Taiwan. Data from 73 typhoons affecting the Shihmen Reservoir watershed were included in the analysis. This study designed six attribute combinations with different lag times for the forecast target. The modified RMSE, bias, and estimated threat score (ETS) results were employed to assess the predicted outcomes. Results show that better attribute combinations for typhoon climatologic characteristics and typhoon precipitation predictions occurred at 0-h lag time with modified RMSE values of 0.288, 0.257, and 0.296 in GSVM, WSVM, and the regression, respectively. Moreover, WSVM having average bias and ETS values close to 1.0 gave better predictions than did the GSVM and regression models. In addition, Typhoons Zeb (1998) and Nari (2001) were selected for comparison between the MM5 model output and the developed statistical models. Results showed that the MM5 tended to overestimate the peak and cumulative rainfall amounts while the statistical models were inclined to yield underestimations.

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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