The Impact of Variational Assimilation of SSM/I and QuikSCAT Satellite Observations on the Numerical Simulation of Indian Ocean Tropical Cyclones

Abstract In this study, the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with three-dimensional variational data assimilation (3DVAR) is utilized to investigate the influence of Special Sensor Microwave Imager (SSM/I) and Quick Scatterometer (QuikSCAT) observations on the prediction of an Indian Ocean tropical cyclone. The 3DVAR sensitivity runs were conducted separately with QuikSCAT wind vectors, SSM/I wind speeds, and total precipitable water (TPW) to investigate their individual impact on cyclone intensity and track. The Orissa supercyclone over the Bay of Bengal during October 1999 was used for simulation and assimilation experiments. Assimilation of the QuikSCAT wind vector improves the initial position of the cyclone’s center with a position error of 33 km, which was 163 km in the background analysis. Incorporation of QuikSCAT winds was found to increase the air–sea heat fluxes over the cyclonic region, which resulted in the improved simulated intensity when compared with the simulation made without QuikSCAT winds in the initial conditions. The cyclone track improved significantly with assimilation of QuikSCAT wind vectors. The track improvement resulted from relocation of the initial cyclonic vortex after assimilation of QuikSCAT wind vectors. Like QuikSCAT, assimilation of SSM/I wind speeds strengthened the cyclonic circulation in the initial conditions. This increase in the low-level wind speeds enhanced the air–sea exchange processes and improved the simulated intensity of the cyclone. The lack of information about the wind direction from SSM/I prevented it from making much of an impact on track prediction. As compared to the first guess, assimilation of the SSM/I TPW shows a moistening of the lower troposphere over most of the Bay of Bengal except over the central region of the cyclone, where the assimilation of SSM/I TPW reduces the lower-tropospheric moisture. This decrease of moisture in the TPW assimilation experiment resulted in a weak cyclone intensity.

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The Impact of Assimilating SSM/I and QuikSCAT Satellite Winds on Hurricane Isidore Simulations

Monthly Weather Review ◽

10.1175/mwr3283.1 ◽

2007 ◽

Vol 135 (2) ◽

pp. 549-566 ◽

Cited By ~ 46

Author(s):

Shu-Hua Chen

Keyword(s):

Wind Speed ◽

Wind Direction ◽

Mesoscale Model ◽

Three Dimensional ◽

Storm Track ◽

Initial Position ◽

Wind Speeds ◽

The Impact ◽

Quikscat Winds

Abstract Three observational datasets of Hurricane Isidore (in 2002) were analyzed and compared: the Special Sensor Microwave Imager (SSM/I), the Quick Scatterometer (QuikSCAT) winds, and dropsonde winds. SSM/I and QuikSCAT winds were on average about 1.9 and 0.3 m s−1 stronger, respectively, than dropsonde winds. With more than 20 000 points of data, SSM/I wind speed was about 2.2 m s−1 stronger than QuikSCAT. Comparison of the wind direction observed by QuikSCAT with those from the dropsondes showed that the quality of QuikSCAT data is good. The effect of assimilating SSM/I wind speeds and/or QuikSCAT wind vectors for the analysis of Hurricane Isidore was assessed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its three-dimensional variational data assimilation system. For the Hurricane Isidore case study, it was found that the assimilation of either satellite winds strengthened the cyclonic circulation in the analysis. However, the increment of the QuikSCAT wind analysis is more complicated than that from the SSM/I analysis due to the correction of the storm location, a positive result from the assimilation of wind vectors. The increase in low-level wind speeds enhanced the air–sea interaction processes and improved the simulated intensity for Isidore. In addition, the storm structure was better simulated. Assimilation of QuikSCAT wind vectors clearly improved simulation of the storm track, in particular during the later period of the simulation, but lack of information about the wind direction from SSM/I data prevented it from having much of an effect. Assessing the assimilation of QuikSCAT wind speed versus wind vector data confirmed this hypothesis. The track improvement partially resulted from the relocation of the storm’s initial position after assimilation of the wind vectors. For this case study, it was found that the assimilation of SSM/I or QuikSCAT data had the greatest impact on the Hurricane Isidore simulation during the first 2 days.

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Adjoint-Derived Impact of Assimilated Observations on Tropical Cyclone Intensity Forecasts of Hurricane Joaquin (2015) and Hurricane Matthew (2016)

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0006.1 ◽

2020 ◽

Vol 37 (8) ◽

pp. 1333-1352

Author(s):

Brett T. Hoover ◽

Chris S. Velden

Keyword(s):

Tropical Cyclone ◽

Initial Conditions ◽

Lower Troposphere ◽

Extended Period ◽

Precipitable Water ◽

Cyclone Intensity ◽

Model Background ◽

Tropical Cyclone Intensity Forecasts ◽

Observation Impact ◽

The Impact

AbstractThe adjoint-derived observation impact method is used as a diagnostic to derive the impact of assimilated observations on a metric representing the forecast intensity of a tropical cyclone (TC). Storm-centered composites of observation impact and the model background state are computed across 6-hourly analysis/forecast cycles to compute the composite observation impact throughout the life cycle of Hurricane Joaquin (2015) to evaluate the impact of in situ wind and temperature observations in the upper and lower troposphere, as well as the impact of brightness temperature and precipitable water observations, on intensity forecasts with forecast lengths from 12 to 48 h. The compositing across analysis/forecast cycles allows for the exploration of consistent relationships between the synoptic-scale state of the initial conditions and the impact of observations that are interpreted as flow-dependent interactions between model background bias and correction by assimilated observations on the TC intensity forecast. The track of Hurricane Matthew (2016), with an extended period of time near the coasts of Florida, Georgia, and the Carolinas, allows for a comparison of the impact of aircraft reconnaissance observations with the impact of nearby overland rawinsonde observations available within the same radius of the TC.

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Impact of Parameterization of Physical Processes on Simulation of Track and Intensity of Tropical Cyclone Nargis (2008) with WRF-NMM Model

The Scientific World JOURNAL ◽

10.1100/2012/671437 ◽

2012 ◽

Vol 2012 ◽

pp. 1-18 ◽

Cited By ~ 19

Author(s):

Sujata Pattanayak ◽

U. C. Mohanty ◽

Krishna K. Osuri

Keyword(s):

Land Surface ◽

Mesoscale Model ◽

Initial Conditions ◽

Surface Wind ◽

India Meteorological Department ◽

Heat Fluxes ◽

Cumulus Convection ◽

Cyclonic Storm ◽

Parameterization Schemes ◽

Cyclone Nargis

The present study is carried out to investigate the performance of different cumulus convection, planetary boundary layer, land surface processes, and microphysics parameterization schemes in the simulation of a very severe cyclonic storm (VSCS) Nargis (2008), developed in the central Bay of Bengal on 27 April 2008. For this purpose, the nonhydrostatic mesoscale model (NMM) dynamic core of weather research and forecasting (WRF) system is used. Model-simulated track positions and intensity in terms of minimum central mean sea level pressure (MSLP), maximum surface wind (10 m), and precipitation are verified with observations as provided by the India Meteorological Department (IMD) and Tropical Rainfall Measurement Mission (TRMM). The estimated optimum combination is reinvestigated with six different initial conditions of the same case to have better conclusion on the performance of WRF-NMM. A few more diagnostic fields like vertical velocity, vorticity, and heat fluxes are also evaluated. The results indicate that cumulus convection play an important role in the movement of the cyclone, and PBL has a crucial role in the intensification of the storm. The combination of Simplified Arakawa Schubert (SAS) convection, Yonsei University (YSU) PBL, NMM land surface, and Ferrier microphysics parameterization schemes in WRF-NMM give better track and intensity forecast with minimum vector displacement error.

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Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-30457-2011 ◽

2011 ◽

Vol 11 (11) ◽

pp. 30457-30485 ◽

Cited By ~ 3

Author(s):

P. Groenemeijer ◽

G. C. Craig

Keyword(s):

Large Scale ◽

Mesoscale Model ◽

Initial Conditions ◽

Deep Convection ◽

Horizontal Resolution ◽

Ensemble Forecasting ◽

Total Variance ◽

Ensemble Prediction ◽

Ensemble Prediction System ◽

The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

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Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

Nonlinear Processes in Geophysics ◽

10.5194/npg-21-1027-2014 ◽

2014 ◽

Vol 21 (5) ◽

pp. 1027-1041 ◽

Cited By ~ 6

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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The impact of SST front on the surface wind in the southern Indian Ocean

10.5194/egusphere-egu2020-7987 ◽

2020 ◽

Author(s):

Xuhua Cheng

Keyword(s):

Indian Ocean ◽

Deep Convection ◽

Surface Wind ◽

Polar Front ◽

Open Ocean ◽

Heat Fluxes ◽

Southern Indian Ocean ◽

Sst Front ◽

Microwave Imager ◽

The Impact

&#160;Using 28-year satellite-borne Special Sensor Microwave Imager observations, features of high-wind frequency (HWF) overthe southern Indian Ocean are investigated. Climatology maps show that high winds occur frequently during austral winter,located in the open ocean south of Polar Front in subpolar region, warm flank of the Subantarctic Front between 55oE-78oE,&#160;and south of Cape Agulhas, where westerly wind prevails. The strong instability of marine atmospheric boundary layeraccompanied by increased sensible and latent heat fluxes on the warmer flank acts to enhance the vertical momentum mixing,thus accelerate the surface winds. Effects of sea surface temperature (SST) front can even reach the entire troposphereby deep convection. HWF also shows distinct interannual variability, which is associated with the Southern Annual Mode(SAM). During positive phase of the SAM, HWF has positive anomalies over the open ocean south of Polar Front, whilehas negative anomalies north of the SST front. A phase shift of HWF happened around 2001, which is likely related to thereduction of storm tracks and poleward shift of westerly winds in the Southern Hemisphere.

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Validation and Interpretation of Adjoint-Derived Sensitivity Steering Vector as Targeted Observation Guidance

Monthly Weather Review ◽

10.1175/2011mwr3490.1 ◽

2011 ◽

Vol 139 (5) ◽

pp. 1608-1625 ◽

Cited By ~ 7

Author(s):

Shin-Gan Chen ◽

Chun-Chieh Wu ◽

Jan-Huey Chen ◽

Kun-Hsuan Chou

Keyword(s):

Initial Conditions ◽

Deep Layer ◽

Lower Troposphere ◽

Subtropical High ◽

Integration Time ◽

Steering Vector ◽

Steering Flow ◽

Targeted Observation ◽

Low Sensitivity ◽

The Impact

The adjoint-derived sensitivity steering vector (ADSSV) has been proposed and applied as a guidance for targeted observation in the field programs for improving tropical cyclone predictability, such as The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC). The ADSSV identifies sensitive areas at the observing time to the steering flow at the verifying time through adjoint calculation. In addition, the ability of the ADSSV to represent signals of influence from synoptic systems such as the midlatitude trough and the subtropical high prior to the recurvature of Typhoon Shanshan (2006) has also been demonstrated. In this study, the impact of initial perturbations associated with the high or low ADSSV sensitivity on model simulations is investigated by systematically perturbing initial vorticity fields in the case of Shanshan. Results show that experiments with the perturbed initial conditions located in the high ADSSV area (i.e., the midlatitude trough and the subtropical high) lead to more track deflection relative to the unperturbed control run than experiments with perturbations in the low sensitivity area. The evolutions of the deep-layer-mean steering flow and the direction of the ADSSV are compared to provide conceptual interpretation and validation on the physical meaning of the ADSSV. Concerning the results associated with the perturbed regions in high sensitivity regions, the variation of the steering flow within the verifying area due to the initial perturbations is generally consistent with that of the direction of the ADSSV. In addition, the bifurcation between the ADSSV and the steering change becomes larger with the increased integration time. However, the result for the perturbed region in the low-sensitivity region indicates that the steering change does not have good agreement with the ADSSV. The large initial perturbations to the low-sensitivity region may interact with the trough to the north due to the nonlinearity, which may not be accounted for in the ADSSV. Furthermore, the effect of perturbations specifically within the sensitive vertical layers is investigated to validate the vertical structure of the ADSSV. The structure of kinetic energy shows that the perturbation associated with the trough (subtropical high) specifically in the mid-to-upper (mid-to-lower) troposphere evolves similarly to that in the deep-layer troposphere, leading to comparable track changes. A sensitivity test in which perturbations are locally introduced in a higher-sensitivity area is conducted to examine the different impact as compared to that perturbed with the broader synoptic feature.

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Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

Nonlinear Processes in Geophysics Discussions ◽

10.5194/npgd-1-917-2014 ◽

2014 ◽

Vol 1 (1) ◽

pp. 917-952

Author(s):

K. Apodaca ◽

M. Zupanski ◽

M. DeMaria ◽

J. A. Knaff ◽

L. D. Grasso

Keyword(s):

Data Assimilation ◽

Prediction Models ◽

Mesoscale Model ◽

Initial Conditions ◽

Positive Impact ◽

Weather Prediction ◽

New Information ◽

Forecasting System ◽

Maximum Likelihood Ensemble Filter ◽

The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

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The impact of northern Indian Ocean rivers on the Bay of Bengal using NEMO global ocean model

Acta Oceanologica Sinica ◽

10.1007/s13131-020-1537-9 ◽

2020 ◽

Vol 39 (3) ◽

pp. 45-55

Author(s):

Atul Srivastava ◽

Anitha Gera ◽

Imran M. Momin ◽

Ashis Kumar Mitra ◽

Ankur Gupta

Keyword(s):

Indian Ocean ◽

Bay Of Bengal ◽

Ocean Model ◽

Global Ocean ◽

Northern Indian Ocean ◽

The Impact

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The impact of diurnal precipitation over Sumatra Island, Indonesia, on synoptic disturbances and its relation to the Madden-Julian Oscillation

10.5194/egusphere-egu21-3677 ◽

2021 ◽

Author(s):

Ayako Seiki ◽

Satoru Yokoi ◽

Masaki Katsumata

Keyword(s):

Indian Ocean ◽

Diurnal Cycle ◽

Lower Troposphere ◽

Reanalysis Data ◽

Eastern Indian Ocean ◽

Sumatra Island ◽

The Indian Ocean ◽

Precipitation Area ◽

High Precipitation ◽

The Impact

The impact of diurnal precipitation over Sumatra Island, the Indonesian Maritime Continent (MC), on synoptic disturbances over the eastern Indian Ocean is examined using high-resolution rainfall data from the Global Satellite Mapping of Precipitation project and the Japanese 55-year Reanalysis data during the rainy season from September to April for the period 2000&#8211;2014. When the diurnal cycle is strong, the high precipitation area observed over Sumatra in the afternoon migrates offshore during nighttime and reaches 500 km off the coast on average. The strong diurnal events are followed by the development of synoptic disturbances over the eastern Indian Ocean for several days, and apparent twin synoptic disturbances straddling the equator develop only when the convective center of the Madden&#8211;Julian Oscillation (MJO) lies over the Indian Ocean (MJO-IO). Without the MJO, the synoptic disturbances develop mainly south of the equator. The differences in the locations and behaviors of active synoptic disturbances are related to the strength of mean horizontal winds in the lower troposphere. During the MJO-IO, the intensification of mean northeasterly winds in the northern hemisphere blowing into the organized MJO convection in addition to mean southeasterly winds in the southern hemisphere facilitate the formation of the twin disturbances. These results suggest that seed disturbances arising from the diurnal offshore migration of precipitation from Sumatra develop differently depending on the mean states over the eastern Indian Ocean. Furthermore, it is shown that the MJO events with the strong diurnal cycle tend to have longer duration and continuing eastward propagation of active convection across the MC, whereas the convective activities of the other MJO events weaken considerably over the MC and develop again over the western Pacific. These results suggest that the strong diurnal cycle over Sumatra facilitates the smooth eastward propagation of the intraseasonal convection across the MC.

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