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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

Abstract

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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Clark Amerault and Xiaolei Zou

Abstract

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 6-h 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 6 h 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.

Open access
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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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.

Open access
Zhengkun Qin, Xiaolei Zou, and Fuzhong Weng

Abstract

The Geostationary Operational Environmental Satellites (GOES) provide high-resolution, temporally continuous imager radiance data over the West Coast (GOES-West currently known as GOES-11) and East Coast (GOES-East currently GOES-12) of the United States. Through a real case study, benefits of adding GOES-11/12 imager radiances to the satellite data streams in NWP systems for improved coastal precipitation forecasts are examined. The Community Radiative Transfer Model (CRTM) is employed for GOES imager radiance simulations in the National Centers for Environmental Prediction (NCEP) gridpoint statistical interpolation (GSI) analysis system. The GOES imager radiances are added to conventional data for coastal quantitative precipitation forecast (QPF) experiments near the northern Gulf of Mexico and the derived precipitation threat score was compared with those from six other satellite instruments. It is found that the GOES imager radiance produced better precipitation forecasts than those from any other satellite instrument. However, when GOES imager radiance and six different types of satellite instruments are all assimilated, the score becomes much lower than the individual combination of GOES and any other instrument. Our analysis shows that an elimination of Advance Microwave Sounding Unit-B (AMSU-B)/Microwave Humidity Sounder (MHS) data over areas where GOES detects clouds significantly improved the forecast scores from AMSU-B/MHS data assimilation.

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Keyun Zhu, I. Michael Navon, and Xiaolei Zou

Abstract

The adjoint model of a finite-element shallow-water equations model was obtained with a view to calculate the gradient of a cost functional in the framework of using this model to carry out variational data assimilation (VDA) experiments using optimal control of partial differential equations.

The finite-element model employs a triangular finite-element Galerkin scheme and serves as a prototype of 2D shallow-water equation models with a view of tackling problems related to VIDA with finite-element numerical weather prediction models. The derivation of the adjoint of this finite-element model involves overcoming specific computational problems related to obtaining the adjoint of iterative procedures for solving systems of nonsymmetric linear equations arising from the finite-element discretization and dealing with irregularly ordered discrete variables at each time step.

The correctness of the adjoint model was verified at the subroutine, level and was followed by a gradient cheek conducted once the full adjoint model was assembled. VDA experiments wore performed using model-generated observations. In our experiments, assimilation was carried out assuming that observations consisting of a full-model-state vector are available at every time step in the window of assimilation. Successful retrieval was obtained using the initial conditions as control variables, involving the minimization of a cost function consisting of the weighted sum of difference between model solution and model-generated observations.

An additional set of experiments was carried out aiming at evaluating the impact of carrying out VDA involving variable mesh resolution in the finite-element model over the entire assimilation period. Several conclusions are drawn related to the efficiency of VDA with variable horizontal mesh resolution finite-element discretization and the transfer of information between coarse and fine meshes.

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Jiaxin Feng, Zhaohua Wu, and Xiaolei Zou

Abstract

Many recent studies have shown that observed El Niño–Southern Oscillation (ENSO) events are spatially and temporally diverse and that they have undergone changes in characteristics. To quantitatively capture these features, multidimensional ensemble empirical mode decomposition (MEEMD) is employed to isolate the temporal–spatial evolution of the sea surface temperature anomalies (SSTAs) on naturally separated time scales. An alternative Niño-3.4 index is also defined to reflect more on the interannual variability of equatorial Pacific SSTAs. Using this alternative index, 27 ENSO warm events are identified and the spatial–temporal evolution of each event is examined. It is found that a patch of SSTAs off Baja California appears to extend southwestward and reach the equatorial region near the international date line in about 1 year. This warm signal then amplifies and extends eastward, developing into an ENSO warm event. This type of development has been dominant in recent decades. For this type of ENSO warm event, it appears that SSTAs off Baja California are instrumental to ENSO development, possibly serving as a precursor of an ENSO event.

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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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