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Xiaoxu Tian and Xiaolei Zou

Abstract

Global observations from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-Orbiting Partnership satellite are affected by striping-patterned noise. An optimal symmetric filter method to mitigate the striping noise in warm counts, cold counts, warm load temperatures, and scene counts instead of antenna temperatures is developed and tested in this study. The optimal filters are developed based on the results free of striping noise obtained with a striping noise detecting method by combining the principal component analysis and the ensemble empirical mode decomposition. The two-point algorithm is then used to calculate antenna temperatures with warm counts, cold counts, warm load temperatures, and scene counts before and after applying the optimal filters. The necessity of applying the striping noise mitigation to the scene counts besides the calibration counts (warm and cold counts) is also shown. This explains why the traditional method to smooth only calibration counts has failed to remove the ATMS striping noise. The optimal filters proposed in this study, which remove the high-frequency striping noise without altering low-frequency weather signals, outperform the conventional boxcar filters adopted in the current operational ATMS calibration system.

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Xiaolei Zou and Qingnong Xiao

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A bogus data assimilation (BDA) scheme is presented and used to generate the initial structure of a tropical cyclone for hurricane prediction. It was tested on Hurricane Felix (1995) in the Atlantic Ocean during its mature stage. The Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model version 5 was used for both the data assimilation and prediction. It was found that a dynamically and physically consistent initial condition describing the dynamic and thermodynamic structure of a hurricane vortex can be generated by fitting the forecast model to a specified bogus surface low based on a few observed and estimated parameters. Through forecast model constraint, BDA is able to recover many of the structural features of a mature hurricane including a warm-core vortex with winds swirling in and out of the vortex center in the lower and upper troposphere, respectively; the eyewall; the saturated ascent around the eye and descent or weak ascent in the eye; and the spiral cloud bands and rainbands. Satellite and radar data, if available, can be incorporated into the BDA procedure. It was shown that satellite-derived water vapor winds have an added value for BDA—they can generate a more realistic initial vortex.

As a result of BDA using both a bogus surface low and satellite water vapor wind data, dramatic improvements occurred in the hurricane prediction of Felix. First of all, the initial fields of model variables describing the BDA initial vortex are well adapted to the forecast model. Second, the intensity forecast was greatly improved. The mean error of the central sea level pressure during the entire 72-h forecast period reduced from 25.9 hPa without BDA to less than 2.1 hPa with BDA. Third, the model captured the structures of the storm reasonably well. In particular, the model reproduced the ring of maximum winds, the eye, the eyewall, and the spiral cloud bands. Finally, improvement in the track prediction was also observed. The 24-, 48-, and 72-h forecast track errors with BDA were 76, 76, and 84 km, respectively, compared to the track errors of 93, 170, and 193 km without BDA.

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Xiaoxu Tian and Xiaolei Zou

Abstract

A recently refined hurricane warm-core retrieval algorithm was applied to data from multiple polar-orbiting satellites that carry the Advanced Technology Microwave Sounder (ATMS) and the Advanced Microwave Sounding Unit-A (AMSU-A) to examine the diurnal variability of the warm cores of Hurricanes Irma and Maria. These hurricanes occurred during the 2017 hyperactive Atlantic hurricane season. Compared with data gathered by dropsondes within 100–1700 km of Hurricanes Irma and Harvey, the means and standard deviations of the differences between ATMS-derived and dropsonde-measured temperature profiles were less than 0.7 and 1 K, respectively, in the vertical layer between ~180 and 750 hPa. The temporal evolutions of the ATMS-derived and AMSU-A-derived maximum warm-core temperature anomalies followed more closely that of the minimum mean sea level pressure and slightly less closely that of the maximum sustained wind. The radii of the ATMS-derived warm cores at 4 and 6 K compared favorably with the 34- and 50-kt-wind radii, respectively, of Hurricane Irma (1 kt = 0.51 m s−1). The vertical extent of the warm core toward lower levels increased with increasing intensity when Hurricane Irma experienced a strong intensification because of an enhanced latent heat release associated with diabatic processes. The tropical cyclone (TC) inner cores at upper-tropospheric levels (~250 hPa) were characterized by a single-peaked diurnal cycle with a maximum around midnight. This warm-core cycle may be an important element of TC dynamics and may have relevance to TC structural and intensity changes.

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Juan Li and Xiaolei Zou

Abstract

A quality control (QC) procedure for satellite radiance assimilation is proposed and applied to radiance observations from the Microwave Temperature Sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-3 series (FY-3A). A cloud detection algorithm is incorporated based on the cloud fraction product provided by the Visible and Infrared Radiometer (VIRR) on board FY-3A. Analysis of the test results conducted in July 2011 indicates that most clouds are identifiable by applying an FY-3A VIRR cloud fraction threshold of 37%. This result is verified with the cloud liquid water path data from the Meteorological Operational Satellite A (MetOp-A). On average, 56.1% of the global MWTS data are identified as cloudy by the VIRR-based cloud detection method. Other QC steps include the following: (i) two outmost field of views (FOVs), (ii) use of channel 3 if the terrain altitude is greater than 500 m, (iii) channel 2 over sea ice and land, (iv) coastal FOVs, and (v) outliers with large differences between model simulations and observations. About 82%, 74%, and 29% of the MWTS observations are removed by the proposed QC for channels 2–4, respectively. An approximate 0.5-K scan bias improvement is achieved with QC, with a large impact at edges of the field of regard for channels 2–4. After QC, FY-3A MWTS global data more closely resemble the National Centers for Environmental Prediction (NCEP) forecast data, the global biases and standard deviations are reduced significantly, and the frequency distribution of the differences between observations and model simulations become more Gaussian.

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Clark Amerault and Xiaolei Zou

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A radiative transfer model was updated to better simulate Special Sensor Microwave Imager (SSM/I)–observed brightness temperatures in areas of high ice concentration. The difference between the lowest observed and model-produced brightness temperatures at 85 GHz has been reduced from over 100 K to roughly 20 K. Probability distribution functions of model-produced and SSM/I-observed brightness temperatures show that the model overestimates the areas of precipitation, but overall matches the SSM/I observations quite well.

Estimates of vertical background error covariance matrices and their inverses were calculated for all hydrometeor variables (both liquid and frozen). For cloud and rainwater, the largest values in the matrices are located in the lower levels of the atmosphere, while the largest values in the cloud ice, snow, and graupel matrices are in the upper levels of the atmosphere. The inverse background matrices can be used as weightings for hydrometeor variables in assimilation experiments involving rain-affected observations.

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Xiaoxu Tian and Xiaolei Zou

Abstract

A four-dimensional variational (4D-Var) data assimilation (DA) system is developed for the global nonhydrostatic atmospheric dynamical core of the Model for Prediction Across Scales (MPAS). The nonlinear forward and adjoint models of the MPAS-Atmosphere dynamic core are included in a Python-driven structure to formulate a continuous 4D-Var DA system, shown to effectively minimize the cost function that measures the distances between the nonlinear model simulations and observations. In this study, three idealized experiments with a six-hour assimilation window are conducted to validate and demonstrate the numerical feasibilities of the 4D-Var DA system for both uniform- and variable-resolution meshes. In the first experiment, only a single point observation is assimilated. The resulting solution shows that the analysis increments have highly flow-dependent features. The observations in the second experiment are all model prognostic variables that span the entire global domain, the purpose of which is to check how well the initial conditions six hours prior to the observations can be reversely inferred. The differences between the analysis and the referenced "truth" are significantly smaller than those calculated with the first guess. The third experiment assimilates the mass field only, i.e., potential temperatures in the case of MPAS-Atmosphere, and examines the impacts on the wind field and the mass field under initial conditions. Both the wind vectors and potential temperatures in the analysis agree more with the referenced "truth" than the first guess because the adjustments made to the initial conditions are dynamically consistent in the 4D-Var system.

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Xiaolei Zou, Zhengkun Qin, and Fuzhong Weng

Abstract

Satellite microwave humidity sounding data are assimilated through the gridpoint statistical interpolation (GSI) analysis system into the Advanced Research core of the Weather Research and Forecasting (WRF) model (ARW) for a coastal precipitation event. A detailed analysis shows that uses of Microwave Humidity Sounder (MHS) data from both NOAA-18 and MetOp-A results in GSI degraded precipitation threat scores in a 24-h model forecast. The root cause for this degradation is related to the MHS quality control algorithm, which is supposed to remove cloudy radiances. Currently, the GSI cloud detection is based on the brightness temperature differences between observations and the model background state at two MHS window channels. It is found that the GSI quality control algorithm fails to identify some MHS cloudy radiances in cloud edges where the ARW model has no cloud and the water vapor amount is low. A new MHS cloud detection algorithm is developed based on a statistical relationship between three MHS channels and the Geostationary Operational Environmental Satellite (GOES) imager channel at 10.7 μm. The 24-h quantitative precipitation forecast is improved rather than degraded by MHS radiance data assimilation when the new cloud detection algorithm is added to the GSI MHS quality control process. The temporal evolution of 3-h accumulative rainfall distributions compared favorably with that of multisensor NCEP observations and GOES-12 imager observations. The precipitation threat scores are increased by more than 50% after 3–6 h of model forecasts for 3-h rainfall thresholds exceeding 1.0 mm.

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Qingnong Xiao, Xiaolei Zou, and Bin Wang

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The bogus data assimilation (BDA) scheme designed by Zou and Xiao to specify initial structures of tropical cyclones was tested further on the simulation of a landfalling hurricane—Hurricane Fran (1996). The sensitivity of the simulated hurricane track and intensity to the specified radius of maximum wind of the bogus vortex, the resolution of the BDA assimilation model, and the bogus variables specified in the BDA are studied. In addition, the simulated hurricane structures are compared with the available observations, including the surface wind analysis and the radar reflectivity captured onshore during Fran’s landfall.

The sensitivity study of the BDA scheme showed that the simulations of the hurricane track and intensity were sensitive to the size of the specified bogus vortex. Hurricanes with a larger radius of maximum sea level pressure gradient are prone to a more westward propagation. The larger the radius, the weaker the predicted hurricane. Results of the hurricane initial structures and prediction were also sensitive to the bogus variables specified in the BDA. Fitting the model to the bogused pressure data reproduced the hurricane structure of all model variables more efficiently than when fitting it to bogused wind data. Examining the initial conditions produced by the BDA, it is found that the generation and intensity of hurricane warm-core structure in the model initial state was a key factor for the hurricane intensity prediction.

Initialized with the initial conditions obtained by the BDA scheme, the model successfully simulated Hurricane Fran’s landfall, the intensity change, and some inner-core structures. Verified against the surface wind analysis, the model reproduced the distribution of the maximum wind streaks reasonably well. The model-predicted reflectivity field during the landfall of Hurricane Fran resembled the observed radar reflectivity image onshore.

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Clark Amerault, Xiaolei Zou, and James Doyle

Abstract

An adjoint modeling system based upon the Naval Research Laboratory’s Coupled Ocean–Atmosphere Mesoscale Prediction System’s atmospheric component has been developed. The system includes the adjoint model of the explicit moist physics parameterization, which allows for gradients with respect to the initial hydrometeor concentrations to be calculated. This work focuses on the ability of the system to calculate evolved perturbations and gradients for the hydrometeor variables. Tests of the tangent linear and adjoint models for an idealized convective case at high model resolution (4-km horizontal grid spacing) are presented in this study. The tangent linear approximation is shown to be acceptable for all model variables (including the hydrometeors) with sizable perturbations for forecasts of 1 h. The adjoint model was utilized with the same convective case to demonstrate its applicability in four-dimensional variational data assimilation experiments. Identical twin experiments were conducted where the adjoint model produced gradients for all model variables, leading to improved analyses and forecasts. The best agreement between model forecasts and simulated observations occurred when information on all model variables was assimilated. In the case where only conventional data were assimilated, the agreement was not as good in the early forecast period. However, the hydrometeor values spun up quickly, and at later times, the forecast performed almost as well as when all data were assimilated.

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Xiaolei Zou, Zhengkun Qin, and Fuzhong Weng

Abstract

The Geostationary Operational Environmental Satellite (GOES) imager provides observations that are of high spatial and temporal resolution and can be applied for effectively monitoring and nowcasting severe weather events. In this study, improved quantitative precipitation forecasts (QPFs) for three coastal storms over the northern Gulf of Mexico and the East Coast is demonstrated by assimilating GOES-11 and GOES-12 imager radiances into the Weather Research and Forecasting (WRF) model. Both the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) analysis system and the Community Radiative Transfer Model (CRTM) are utilized to ingest GOES IR clear-sky data. Assimilation of GOES imager radiances during a 6–12-h time window prior to convective initiation and/or development could significantly improve the precipitation forecasts near the coast of the northern Gulf of Mexico. The 3-h accumulative precipitation threat scores are increased by about 20% after 6 h of model forecasts and more than 50% after 18–24 h of model forecasts. A detailed diagnosis of analysis fields and model forecast fields is carried out for one of the three convective precipitation events included in this study. It is shown that the assimilation of GOES data in regions of no or little clouds improved the model description of an upstream midlatitude trough and a subtropical high located in the south of the convection. The GOES observations located in the western part of land region covered by GOES within the latitude zone of 18°–37°N near 100°W contributed to a better forecast of the position of the eastward-propagating trough, while GOES observations over the Gulf of Mexico increased the amount of water vapor advection from the south into the convective region by the wind associated with the subtropical high. In the past, GOES imager radiances were not directly used in the GSI system. This study highlights the importance of satellite imagery information observed in the preconvective environment for improved cloud and precipitation forecasts. The developed data assimilation technique will prepare the NWP user community for accelerated use of advanced satellite data from the GOES-R series.

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