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.

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.

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.

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.

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.

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.

Abstract

The Tibetan Plateau is a sensitive area of global climate change, where few conventional observations exist. Satellite AMSU-A microwave temperature sounding observations of brightness temperature (TB) are located in the absorption band of oxygen, which is well mixed in the atmosphere, and have microwave frequencies varying from 50.3 to 57.6 GHz. Therefore, AMSU-A TB observations at different sounding channels reflect atmospheric temperatures at different altitudes. In this study, AMSU-A TB observations during 1998–2020 from five polar-orbiting environmental meteorological satellites (POESs) are employed to investigate the interdecadal warming/cooling trends over the Tibetan Plateau. A limb correction is first applied to all AMSU-A channels before using TB observations at all fields of view for examining geographic distributions and differences of global warming/cooling trends. It is found that interdecadal trends of upper-tropospheric warming and stratospheric cooling are stronger over the Qinghai Tibetan Plateau than its eastern plain areas. An interdecadal variation of the annual cycle over the Tibetan Plateau is an important factor for the enhanced tropospheric warming trend. We also applied a different approach of significance testing that is based on counting signs of local trends (sign test) and confirmed that the detected significant local trends were not a result of chance. In addition, high-frequency noise in TB observations with periods smaller than annual and semiannual oscillations do not affect the climate trends of TB very much, but significantly reduced the uncertainty of the TB trends over the Tibetan Plateau.

Abstract

Bias correction (BC) is a crucial step for satellite radiance data assimilation (DA). In this study, the traditional airmass BC scheme in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) is investigated for Cross-track Infrared Sounder (CrIS) DA. The ability of the airmass predictors to model CrIS biases is diagnosed. Correlations between CrIS observation-minus-background (O − B) samples and the two lapse rate–related airmass predictors employed by GSI are found to be very weak, indicating that the bias correction contributed by the airmass BC scheme is small. A modified BC scheme, which directly calculates the moving average of O − B departures from data of the previous 2 weeks with respect to scan position and latitudinal band, is proposed and tested. The impact of the modified BC scheme on CrIS radiance DA is compared with the variational airmass BC scheme. Results from 1-month analysis/forecast experiments show that the modified BC scheme removes nearly all scan-dependent and latitude-dependent biases, while residual biases are still found in some channels when the airmass BC scheme is applied. Smaller predicted root-mean-square errors of temperature and specific humidity and higher equivalent threat scores are obtained by the DA experiment using the modified BC scheme. If O − B samples are replaced by observation-minus-analysis (O − A) samples for bias estimates in the modified BC scheme, the forecast impacts are reduced but remain positive. A convective precipitation case that occurred on 21 August 2016 is investigated. Using the modified BC scheme, the atmospheric temperature structure and the geopotential height structures near trough/ridge areas are better resolved, resulting in better precipitation forecasts.

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.

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.