This study uses a unique combination of airborne and satellite observations to characterize narrow regions of strong horizontal water vapor flux associated with polar cold fronts that occurred over the eastern North Pacific Ocean during the winter of 1997/98. Observations of these “atmospheric rivers” are compared with past numerical modeling studies to confirm that such narrow features account for most of the instantaneous meridional water vapor transport at midlatitudes. Wind and water vapor profiles observed by dropsondes deployed on 25–26 January 1998 during the California Land-falling Jets Experiment (CALJET) were used to document the structure of a modest frontal system. The horizontal water vapor flux was focused at low altitudes in a narrow region ahead of the cold front where the combination of strong winds and large water vapor content were found as part of a low-level jet. A close correlation was found between these fluxes and the integrated water vapor (IWV) content. In this case, 75%...

https://journals.ametsoc.org/doi/pdf/10.1175/1520-0493%282004%29132%3C1721%3ASACAOO%3E2.0.CO%3B2

Satellite and CALJET Aircraft Observations of Atmospheric Rivers over the Eastern North Pacific Ocean during the Winter of 1997/98

Cloud-top pressures and effective cloud amounts, derived from Geostationary Operational Environmental Satellite (GOES) sounders, are used to specify a three-dimensional mesoscale cloud field for numerical weather prediction. A bulk cloud parameterization scheme based on relative humidity is introduced into the forecast model to efficiently use the satellite sounder observations. The cloud initialization and bulk cloud parameterization are tested in the Cooperative Institute for Meteorological Satellite Studies Regional Assimilation System for summer and winter periods. Comparisons between cloud forecasts and GOES cloud observations for four model configurations are presented. It is shown that the use of the initialization improved the areal coverage of the nonprecipitating clouds early in the forecast period, which translated to an improvement of the precipitation equitable threat score for the 24-h forecast (when the model initial time was 1200 UTC) and the 36-h forecast (when the model initial time was 0000 UTC).

NWP Cloud Initialization Using GOES Sounder Data and Improved Modeling of Nonprecipitating Clouds

The influence of Geostationary Operational Environmental Satellite (GOES) brightness temperature data on the numerical simulations of Hurricane Felix is investigated. Satellite data are included as an augmentation to a bogus data assimilation (BDA) procedure using a mesoscale adjoint modeling system. The assimilation of satellite data modified not only the environmental flow but also the structure of the initial vortex, which is located over a region devoid of satellite data. This modification resulted in a reduction of the 12-h forecast errors verified by radiosonde data. Despite the fact that the forecast using only the bogus surface low at the initial time was very good, track and intensity forecasts beyond 2 days of model integration were shown to be improved further by including satellite data in the initialization procedure. Differences in the prediction of Hurricane Felix with and without satellite data were also found in the prediction of the upper-level jet, the cold temperature trough ahead of the hurricane, the size of the hurricane eye, and the location of the maximum hydrometeor. Although the focus of this study is to assess the effect of the direct use of satellite brightness temperature data on hurricane prediction, it is also noted that the BDA experiment including only the bogus data shows a positive effect of the BDA vortex on the environmental flow during the forecast period, as verified by satellite observations.

A Numerical Study of the Effect of GOES Sounder Cloud-Cleared Brightness Temperatures on the Prediction of Hurricane Felix

In this paper, the three-dimensional variational data assimilation scheme (3DVAR) in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) is used to study the impact of assimilating Atmospheric Infrared Sounder (AIRS) retrieved temperature and moisture profiles on board Aqua, a satellite that is part of NASA’s Earth Observing System. A record-breaking heavy rain event that occurred over Mumbai, India, on 26 July 2005 with 24-h rainfall exceeding 94 cm was used for the simulation. By analyzing the data from the NCEP–NCAR reanalysis, possible causes of this heavy rainfall event were investigated. The temporal evolution of meteorological fields clearly indicates the formation of midtropospheric mesoscale vortices over Mumbai that exactly coincides with the duration of the intense rainfall. Analysis also indicated the midlevel dryness with higher temperature and moisture in the lower levels. This midlevel dryness with high...

Impact of Atmospheric Infrared Sounder Data on the Numerical Simulation of a Historical Mumbai Rain Event

The highest correlative relations for air pollution levels are often with meteorological variables such as temperature and wind speed. Today, sophisticated gridded high-resolution meteorological models are used to produce meteorological fields that drive chemical transport models for air quality management. Errors in specification of the physical atmosphere such as temperature, clouds and winds can affect the air quality predictions. Additionally, the efficiency and efficacy of emission control strategies can be compromised by errors in the meteorological fields. In this paper, the role of meteorology in air quality behavior, primarily from the viewpoint of regional ozone modeling as carried out in the U.S., is reviewed. Particular attention is given to physics and new techniques for improving meteorological model performance. Uncertainties in model turbulent mixing in the nighttime boundary layer, where large model differences exist, are examined. The role of spatial mesoscale features such as topography and land/water systems in models are discussed. The nocturnal low-level jet, a mesoscale temporal and spatial feature, and its impact on air quality are examined. Traditional air quality concerns have focused on synoptic conditions at the center of high-pressure systems. However, high ozone levels have also been associated with stationary fronts. The ability of models to capture mesoscale structure and yet retain synoptic structure and its timing is challenging. Data assimilation and its ability to improve model performance are examined. Particular attention is given to vertical nudging strategies that can affect formation of the nocturnal low-level jets. Finally, clouds can have a major impact on air quality since insolation impacts temperature, biogenic emissions and photolysis rates and extremes in stability. Traditional techniques, which attempt to insert cloud water where there is not dynamical support, can lead to additional errors. New dynamical approaches for improving model cloud performance are discussed.Implications: This article shows that there has been a considerable improvement in meteorological models used for air quality simulations. In particular, improvement in the tools for incorporating both traditional observations and new satellite data for retrospective studies has been beneficial to air quality community. However, while this trend is continuing, many challenges remain. As an example, due to having many options available in configuring a model simulation, there is a need to evaluate and recommend sets of options that provide important performance measures.

https://www.tandfonline.com/doi/pdf/10.1080/10962247.2019.1694602

Meteorological modeling relevant to mesoscale and regional air quality applications: a review.

Assimilation of satellite data can enhance the ability of a mesoscale modeling system to produce accurate short-term forecasts of clouds and precipitation, but only if there is a mechanism for the satellite-derived information to propagate coherently from the analysis into the forecast period. In situations where stratiform cloud cover inhibits surface heating, assimilation of visible image data can be beneficial for analyses, but those data present particular challenges for application to numerical forecasts. To address the forecast problem, a method to adjust the humidity field and the radiative parameterization of a model was developed such that satellite retrievals of cloud properties have an impact that extends well into the forecast. The adjustment directs the model’s cloud diagnosis and radiation algorithms to produce results that agree with satellite retrievals valid at the forecast initiation time. Experiments showed a high level of fidelity between a short-term forecast made with this m...

Assimilation of Visible-Band Satellite Data for Mesoscale Forecasting in Cloudy Conditions

A satellite–model coupled procedure for assimilating geostationary satellite sounder data was adapted to a mesoscale analysis and forecast system jointly developed by the Naval Research Laboratory and the Air Force Research Laboratory. The coupled procedure involves the use of the model output fields as the first guess for the thermodynamic retrievals. Atmospheric thermodynamic profiles and ground temperatures were retrieved from observed radiances of the VISSR Atmospheric Sounder (VAS) on board the Geostationary Operational Environmental Satellite. The successive corrections objective analysis scheme in the mesoscale analysis and forecast system was modified to consider the horizontal spatial correlation of the satellite data. The procedure was tested using a wintertime case from the 1986 Genesis of Atlantic Lows Experiment project. The retrievals generated by the coupled method were modestly improved relative to independent stand-alone retrievals. Coupled analyses and forecasts of temperature a...

https://journals.ametsoc.org/doi/pdf/10.1175/1520-0493%281999%29127%3C0802%3ACAOGSS%3E2.0.CO%3B2

Coupled Assimilation of Geostationary Satellite Sounder Data into a Mesoscale Model Using the Bratseth Analysis Approach

Geostationary satellite sounder radiances have typically been averaged over several individual fields of view before the radiances are used for retrieving thermodynamic profiles or for assimilation in weather prediction models. The purpose of the averaging is to compensate for data noise. Cloudy fields of view are excluded from averaging. In areas without a sufficient number of clear fields of view, complete profiles are not retrieved. Clouds thus cause gaps in sounder coverage. This note describes an automated method to select a set of averaging areas for a given field of sounder data, such that the gaps in coverage caused by clouds are as small and as few as possible. Test results are shown, indicating that the method can provide substantially better coverage than is obtained with a commonly used method.

Improved GOES Sounder Coverage of Cloud-Broken Data Fields

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Assimilation of Visible-Band Satellite Data for Mesoscale Forecasting in Cloudy Conditions

Coupled Assimilation of Geostationary Satellite Sounder Data into a Mesoscale Model Using the Bratseth Analysis Approach

Improved GOES Sounder Coverage of Cloud-Broken Data Fields