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X. Zhuge and X. Zou

Abstract

Assimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

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C. Amerault and X. Zou

Abstract

Different aspects of assimilating satellite-observed microwave radiances (brightness temperatures) into the initial vortex of a hurricane prediction model are discussed. The tangent linear and adjoint observation operators were developed from a computationally inexpensive and reasonably accurate radiative transfer model. These models have the advantage of being able to perform in all types of weather, including rain. The adjoint radiative transfer model was used to conduct a sensitivity analysis of brightness temperatures to different atmospheric and surface variables. The sensitivities computed by the model compare favorably with physical understandings of how brightness temperatures are affected by the atmosphere and the surface. The errors associated with some of the approximations in the radiative transfer model were estimated from comparisons with a more accurate model. These errors were found to be smaller than estimates from previous studies. The random errors associated with brightness temperature observations were also estimated from statistical structure function calculations and were found to be in line with estimates previously used. The models developed and the errors calculated for this study will be used in future work to assimilate brightness temperatures in hurricane initializations and to evaluate the performance of different microphysical schemes in hurricane prediction.

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Y. Hu and X. Zou

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Determining tropical cyclone (TC) center positions is of interest to many researchers who conduct TC analysis and forecasts. In this study, we develop and apply a TC centering technique to Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) observations of brightness temperature and report on an improvement of accuracy by adding a TC spectral analysis to the state of the art [Automated Rotational Center Hurricane Eye Retrieval (ARCHER)], especially for ATMS. We show that the ARCHER TC center-fixing algorithm locates TC centers more successfully based on the infrared channel with center frequency at 703.75 cm−1 (channel 89) of the CrIS than the ATMS channel 22 (183.31 ± 1.0 GHz) due to small-scale features in ATMS channel’s brightness temperature field associated with strong convective clouds. We propose to first apply the ARCHER TC center-fixing algorithm to ATMS channel 4 (51.76 GHz) that is less affected by small-scale convective clouds, and then to perform a set of the azimuthal spectral analysis of the ATMS channel-22 observations with tryout centers within a squared box centered at the ATMS channel-4-determined center. The center that gives the largest symmetric component is the final ATMS-determined center. Compared to the National Hurricane Center best track, the root-mean-square center-fixing errors determined from the two ATMS channels (one single CrIS channel) are 29.9 km (35.8 km) and 28.0 km (30.9 km) for 104 tropical storm and 81 hurricane cases, respectively, in the 2019 hurricane season.

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Zhen Zeng and X. Zou

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A principal component analysis (PCA) method is applied to Challenging Minisatellite Payload (CHAMP) level-2 radio occultation (RO) observations and the corresponding global analyses from the National Centers for Environmental Prediction (NCEP) in March 2004. The PCA is performed on a square symmetric vertical correlation matrix of observed or modeled RO profiles. By decomposing the matrix into pairs of loadings (EOFs) and associated principal components (PCs), outliers are identified and important modes that explain most variances of the vertical variability of the atmosphere as represented by the GPS RO data and the NCEP analyses are extracted and compared. Specifically, a quality control of RO data based on Hotelling’s T2 index is applied first, which removes 255 RO profiles from 4884 total profiles (about 5%) and smoothes the distributions of PC modes, making the remaining GPS RO dataset much more meaningful. The leading PC mode for global refractivity explains 60% of the total variance and is associated with a symmetric zonal pattern, with positive anomalies in the Tropics and negative anomalies at the two poles. The second PC mode explains an additional 16% of the total variance and shows a dipole pattern with positive anomalies in the North Pole and negative anomalies in the South Pole. Three significant positive anomalies are also found in the second and third PC modes over three predominant convective areas in the western Pacific, South America, and Africa in the Tropics. The first leading PC mode calculated from global NCEP analyses compared favorably with that from CHAMP observations, which proves that NCEP analyses are capable of representing most of the variance of the atmospheric profiles. However, disagreements between CHAMP observations and NCEP analyses are noticed in the second EOF over the Tropics and the Southern Hemisphere (SH). It is also found that the NCEP analyses describe CHAMP-observed larger vertical scale features better than smaller-scale features, captures features of more leading EOF modes in the Northern Hemisphere than in the SH and the Tropics, and does not capture the vertical structures revealed by the EOFs in CHAMP observations near and above the tropopause in the Tropics.

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Kyungjeen Park and X. Zou

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This study aims to make the four-dimensional variational (4DVAR) bogus data assimilation (BDA) scheme for hurricane initialization first proposed by Zou and Xiao more objective. The BDA scheme consists of two steps: (i) specifying a bogus sea level pressure (SLP) field based on parameters observed by the Tropical Prediction Center (TPC) and (ii) assimilating the bogus SLP field under a forecast model constraint adjusting all model variables. In previous studies, specification of the bogus SLP was based on Fujita's formula, requiring the central SLP pressure (P c), the radius of the outermost closed isobar (R out), and the radius of the maximum SLP gradient (R 0) as inputs. Although the parameters P c and R out are provided directly by the TPC, R 0 is not. In this research, an empirical linear model designed to determine the value of R 0 (the size of the bogus vortex) from the TPC observed radius of 34-kt wind (R 34kt) is developed. Numerical experiments are carried out for the initialization and prediction of Hurricane Bonnie (1998) over the Atlantic Ocean. The Pennsylvania State University–NCAR nonhydrostatic mesoscale adjoint modeling system () is used for both the data assimilation and prediction components of the forecast. In order to study the sensitivity of hurricane initialization and prediction to the radial profile specification of the bogus vortex, the same experiment is conducted using Fujita's formula with R 0 = R max (the radius of the TPC observed maximum wind) and another formula, Holland's formula, for the specification of the bogus SLP. The track prediction is less sensitive to the specification of the bogus SLP than the intensity prediction. The maximum track error is less than 110 km during the entire 3-day forecast for any of the three experiments using different bogus SLP specifications. However, the experiment using the linear model for the size specification required by Fujita's formula considerably outperforms the other two formulations for the intensity prediction of Hurricane Bonnie. Diagnosis of model output indicates that the 4DVAR BDA generated an initial hurricane, which allows for larger amounts of surface fluxes of heat and moisture, angular momentum, and latent heat of condensation, supporting a stronger and more realistic hurricane with more realistic intensity changes than experiments using the other two formulations.

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H. Liu and X. Zou

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During the North Pacific Experiment (NORPEX), both the Navy Operational Global Atmospheric Prediction System and the National Centers for Environmental Prediction (NCEP) operational forecast systems found a 48-h forecast degradation over the NORPEX forecast verification region due to the inclusion of a set of NORPEX targeted dropsondes deployed north of Hawaii during 29–30 January 1998. The NCEP three- and four-dimensional varitional data assimilation (3DVAR and 4DVAR) systems are used here to reassess the impact of these dropsonde observations on model predictions. The assimilation of these targeted dropsondes excluding the conventional observations improved the 48-h forecast over the NORPEX forecast verification region. However, the addition of the dropsonde data to an analysis that already contained various conventional observations degraded the 48-h forecast over the NORPEX forecast verification region. In the later case, the dropsonde data still improved and had its largest impact on the forecast over the northeast Pacific (outside of the forecast verification region). In this region, errors in the forecast using only conventional observations were largest. Furthermore, assimilation of the targeted dropsonde data using the 4DVAR approach produced greater improvements in the 1–3-day forecasts over the Pacific Ocean than the 3DVAR approach did in both cases, with and without conventional observations.

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S. Yang and X. Zou

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Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) refractivity profiles in altostratus and nimbostratus clouds from 2007 to 2010 are first identified based on collocated CloudSat data. Vertical temperature profiles in these clouds are then retrieved from cloudy refractivity profiles. Contributions of cloud liquid water content and ice water content are also included in the retrieval algorithm. The temperature profiles and their lapse rates are compared with those from a standard GPS RO wet retrieval without including cloud effects. On average, the temperatures from cloudy retrieval are about 0.5–1.0 K warmer than the GPS RO wet retrieval, except for the altitudes near the nimbostratus base. The differences of temperature between the two methods are largest in summer and smallest in winter. The lapse rate in altostratus clouds is around 6.5°–7.5°C km−1 and does not vary greatly with height. On the contrary, the lapse rate increases significantly with height in nimbostratus clouds, from about 2.5°–3.5°C km−1 near the cloud base to about 5.0°–6.0°C km−1 at cloud center and 6.5°–7.5°C km−1 below the cloud top. Seasonal variability of lapse rate derived from the cloudy retrieval is larger than that derived from the wet retrieval. The lapse rate within clouds is smaller in summer and larger in winter. The mean lapse rate decreases with temperature in all seasons.

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H. Dong and X. Zou

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The Global Precipitation Measurement (GPM) Microwave Imager (GMI) plays an important role in monitoring global precipitation. In this study, an along-track striping noise is found in GMI observations of brightness temperatures for the two highest-frequency channels—12 and 13—with their central frequencies centered at 183.31 GHz. These two channels are designed for sounding the water vapor in the middle and upper troposphere. The pitch maneuver data of deep space confirmed an existence of striping noise in channels 12 and 13. A striping noise mitigation method is used for extracting the striping noise from the earth scene or deep space measurements of brightness temperatures by combining the principle component analysis (PCA) with the ensemble empirical mode decomposition (EEMD) method. A power spectrum density analysis indicated that the frequency of striping noise ranges between 0.06 and 0.533 s−1, where the right bound of 0.533 s−1 of frequency is exactly the inverse of the time (i.e., 1.875 s) it takes for the GMI to complete one conical scan line. The magnitude of striping noise in the brightness temperature observations of GMI channels 12 and 13 is about ±0.3 K. It is shown that after striping noise mitigation, the observation minus model simulation (O − B) distributions of both the earth scene and deep space brightness temperatures show no visible striping features.

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X. Zou, X. Zhuge, and F. Weng

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Starting in 2014, the new generation of Japanese geostationary meteorological satellites carries an Advanced Himawari Imager (AHI) to provide the observations of visible, near infrared, and infrared with much improved spatial and temporal resolutions. For applications of the AHI measurements in numerical weather prediction (NWP) data assimilation systems, the biases of the AHI brightness temperatures at channels 7–16 from the model simulations are first characterized and evaluated using both the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). It is found that AHI biases under a clear-sky atmosphere are independent of satellite zenith angle except for channel 7. The biases of three water vapor channels increase with scene brightness temperatures and are nearly constant except at high brightness temperatures for the remaining infrared channels. The AHI biases at all the infrared channels are less than 0.6 and 1.2 K over ocean and land, respectively. The differences in biases between RTTOV and CRTM with the land surface emissivity model used in RTTOV are small except for the upper-tropospheric water vapor channels 8 and 9 and the low-tropospheric carbon dioxide channel 16. Since the inputs used for simulations are the same for CRTM and RTTOV, the differential biases at the water vapor channels may be associated with subtle differences in forward models.

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X. Zou and Y-H. Kuo

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To assess the impact of rainfall observations on short-range forecasts of precipitation, and to improve our understanding of the physical processes responsible for the development of a mesoscale convective system (MCS) associated with the dryline that occurred on 10 April 1979 in the midwestern United States, a series of four-dimensional variational data assimilation experiments was conducted based on the special dataset collected in the Severe Environmental Storm and Mesoscale Experiment. A nonhydrostatic mesoscale model (MM5) with a relatively simple moist physics and its adjoint were used for both the model simulation and data assimilation.

A previous numerical simulation of this MCS, based on conventional initialization procedures, failed to correctly simulate the location and intensity of the observed rainfall. This is attributed to the lack of mesoscale details in the model's initial conditions for the low-level moisture convergence and the upper-level disturbances related to the upper-level jet streak. In contrast, the initial conditions created by the four-dimensional variational data assimilation method, which incorporated 3-h rainfall data along with wind, temperature, surface moisture, and precipitable water measurements, produced an improved short-range (up to 12 h) rainfall prediction. It also captured many important mesoscale features including the structure of MCSs, the lower- and upper-level jets, the position of the dryline, the low-level moisture convergence, and the formation of a localized capping inversion (lid). In addition, the spinup time required for precipitation was reduced.

Additional experiments were conducted to assess the importance of lateral boundary conditions (LBCs) in the assimilation procedure, the importance of the precipitable water measurements, and the impact of moist physics. In comparison to the experiment in which only initial conditions (ICs) are used as a control variable, controlling both the initial and lateral boundary conditions during the minimization procedure produced a closer match to the observed rainfall while fewer changes are made to the analyzed ICs. The authors showed that assimilating precipitable water into the model is very important. The precipitable water assimilation constrains the large-scale model moisture error growth while allowing the model to generate mesoscale structures through rainfall assimilation. The 4DVAR rainfall assimilation experiments using two different cumulus parameterization schemes produced very similar adjustments to the original analysis, and model forecasts from the “optimal” ICs and LBCs obtained through rainfall assimilation using a cumulus parameterization scheme different from the one used in the 4DVAR procedure were seen to perform better than that from CTRL without 4DVAR.

These results strongly confirm that improved quantitative precipitation forecasts of mesoscale convective systems are possible through the assimilation of rainfall observations, along with other conventional data. Further improvement can be expected with the use of a high-resolution model with improved moist physics and boundary layer parameterization.

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