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Fuqin Li, William P. Kustas, John H. Prueger, Christopher M. U. Neale, and Thomas J. Jackson

Abstract

Two resistance network formulations that are used in a two-source model for parameterizing soil and canopy energy exchanges are evaluated for a wide range of soybean and corn crop cover and soil moisture conditions during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The parallel resistance formulation does not consider interaction between the soil and canopy fluxes, whereas the series resistance algorithms provide interaction via the computation of a within-air canopy temperature. Land surface temperatures were derived from high-resolution Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM) scenes and aircraft imagery. These data, along with tower-based meteorological data, provided inputs for the two-source energy balance model. Comparison of the local model output with tower-based flux observations indicated that both the parallel and series resistance formulations produced basically similar estimates with root-mean-square difference (RMSD) values ranging from approximately 20 to 50 W m−2 for net radiation and latent heat fluxes, respectively. The largest relative difference in percentage [mean absolute percent difference (MAPD)] was for sensible heat flux, which was ≈35%, followed by a MAPD ≈ 25% for soil heat flux, ≈10% for latent heat flux, and a MAPD < 5% for net radiation. Although both series and parallel versions gave similar results, the parallel resistance formulation was found to be more sensitive to model parameter specification, particularly in accounting for the effects of vegetation clumping resulting from row crop planting on flux partitioning. A sensitivity and model stability analysis for a key model input variable, that is, fractional vegetation cover, also show that the parallel resistance network is more sensitive to the errors vegetation cover estimates. Furthermore, it is shown that for a much narrower range in vegetation cover fraction, compared to the series resistance network, the parallel resistance scheme is able to achieve a balance in both the radiative temperature and convective heat fluxes between the soil and canopy components. This result appears to be related to the moderating effects of the air temperature in the canopy air space computed in the series resistance scheme, which represents the effective source height for turbulent energy exchange across the soil–canopy–atmosphere system.

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Keith D. Hutchison, Barbara D. Iisager, Thomas J. Kopp, and John M. Jackson

Abstract

A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

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Keith D. Hutchison, Bruce Hauss, Barbara D. Iisager, Hiroshi Agravante, Robert Mahoney, Alain Sei, and John M. Jackson

Abstract

An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sun-glint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions.

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Keith D. Hutchison, Robert L. Mahoney, Eric F. Vermote, Thomas J. Kopp, John M. Jackson, Alain Sei, and Barbara D. Iisager

Abstract

A geometry-based approach is presented to identify cloud shadows using an automated cloud classification algorithm developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit both the cloud confidence and cloud phase intermediate products generated by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask (VCM) algorithm. The procedures have been tested and found to accurately detect cloud shadows in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are applied over both land and ocean background conditions. These new procedures represent a marked departure from those used in the heritage MODIS cloud mask algorithm, which utilizes spectral signatures in an attempt to identify cloud shadows. However, they more closely follow those developed to identify cloud shadows in the MODIS Surface Reflectance (MOD09) data product. Significant differences were necessary in the implementation of the MOD09 procedures to meet NPOESS latency requirements in the VCM algorithm. In this paper, the geometry-based approach used to predict cloud shadows is presented, differences are highlighted between the heritage MOD09 algorithm and new VIIRS cloud shadow algorithm, and results are shown for both these algorithms plus cloud shadows generated by the spectral-based approach. The comparisons show that the geometry-based procedures produce cloud shadows far superior to those predicted with the spectral procedures. In addition, the new VCM procedures predict cloud shadows that agree well with those found in the MOD09 product while significantly reducing the execution time as required to meet the operational time constraints of the NPOESS system.

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Soroosh Sorooshian, Amir AghaKouchak, Phillip Arkin, John Eylander, Efi Foufoula-Georgiou, Russell Harmon, Jan M. H. Hendrickx, Bisher Imam, Robert Kuligowski, Brian Skahill, and Gail Skofronick-Jackson

No abstract available.

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Soroosh Sorooshian, Amir AghaKouchak, Phillip Arkin, John Eylander, Efi Foufoula-Georgiou, Russell Harmon, Jan M. H. Hendrickx, Bisher Imam, Robert Kuligowski, Brian Skahill, and Gail Skofronick-Jackson

No abstract available.

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Declan L. Finney, John H. Marsham, Lawrence S. Jackson, Elizabeth J. Kendon, David P. Rowell, Penelope M. Boorman, Richard J. Keane, Rachel A. Stratton, and Catherine A. Senior

Abstract

The precipitation and diabatic heating resulting from moist convection make it a key component of the atmospheric water budget in the tropics. With convective parameterization being a known source of uncertainty in global models, convection-permitting (CP) models are increasingly being used to improve understanding of regional climate. Here, a new 10-yr CP simulation is used to study the characteristics of rainfall and atmospheric water budget for East Africa and the Lake Victoria basin. The explicit representation of convection leads to a widespread improvement in the intensities and diurnal cycle of rainfall when compared with a parameterized simulation. Differences in large-scale moisture fluxes lead to a shift in the mean rainfall pattern from the Congo to Lake Victoria basin in the CP simulation—highlighting the important connection between local changes in the representation of convection and larger-scale dynamics and rainfall. Stronger lake–land contrasts in buoyancy in the CP model lead to a stronger nocturnal land breeze over Lake Victoria, increasing evaporation and moisture flux convergence (MFC), and likely unrealistically high rainfall. However, for the mountains east of the lake, the CP model produces a diurnal rainfall cycle much more similar to satellite estimates, which is related to differences in the timing of MFC. Results here demonstrate that, while care is needed regarding lake forcings, a CP approach offers a more realistic representation of several rainfall characteristics through a more physically based realization of the atmospheric dynamics around the complex topography of East Africa.

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Rory G. J. Fitzpatrick, Douglas J. Parker, John H. Marsham, David P. Rowell, Francoise M. Guichard, Chris M. Taylor, Kerry H. Cook, Edward K. Vizy, Lawrence S. Jackson, Declan Finney, Julia Crook, Rachel Stratton, and Simon Tucker

Abstract

Extreme rainfall is expected to increase under climate change, carrying potential socioeconomic risks. However, the magnitude of increase is uncertain. Over recent decades, extreme storms over the West African Sahel have increased in frequency, with increased vertical wind shear shown to be a cause. Drier midlevels, stronger cold pools, and increased storm organization have also been observed. Global models do not capture the potential effects of lower- to midtropospheric wind shear or cold pools on storm organization since they parameterize convection. Here we use the first convection-permitting simulations of African climate change to understand how changes in thermodynamics and storm dynamics affect future extreme Sahelian rainfall. The model, which simulates warming associated with representative concentration pathway 8.5 (RCP8.5) until the end of the twenty-first century, projects a 28% increase of the extreme rain rate of MCSs. The Sahel moisture change on average follows Clausius–Clapeyron scaling, but has regional heterogeneity. Rain rates scale with the product of time-of-storm total column water (TCW) and in-storm vertical velocity. Additionally, prestorm wind shear and convective available potential energy both modulate in-storm vertical velocity. Although wind shear affects cloud-top temperatures within our model, it has no direct correlation with precipitation rates. In our model, projected future increase in TCW is the primary explanation for increased rain rates. Finally, although colder cold pools are modeled in the future climate, we see no significant change in near-surface winds, highlighting avenues for future research on convection-permitting modeling of storm dynamics.

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Wendell A. Nuss, John ML Bane, William T. Thompson, Teddy Holt, Clive E. Dorman, F. Martin Ralph, Richard Rotunno, Joseph B. Klemp, William C. Skamarock, Roger M. Samelson, Audrey M. Rogerson, Chris Reason, and Peter Jackson

Coastally trapped wind reversals along the U.S. west coast, which are often accompanied by a northward surge of fog or stratus, are an important warm-season forecast problem due to their impact on coastal maritime activities and airport operations. Previous studies identified several possible dynamic mechanisms that could be responsible for producing these events, yet observational and modeling limitations at the time left these competing interpretations open for debate. In an effort to improve our physical understanding, and ultimately the prediction, of these events, the Office of Naval Research sponsored an Accelerated Research Initiative in Coastal Meteorology during the years 1993–98 to study these and other related coastal meteorological phenomena. This effort included two field programs to study coastally trapped disturbances as well as numerous modeling studies to explore key dynamic mechanisms. This paper describes the various efforts that occurred under this program to provide an advancement in our understanding of these disturbances. While not all issues have been solved, the synoptic and mesoscale aspects of these events are considerably better understood.

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Lynn A. McMurdie, Gerald M. Heymsfield, John E. Yorks, Scott A. Braun, Gail Skofronick-Jackson, Robert M. Rauber, Sandra Yuter, Brian Colle, Greg M. McFarquhar, Michael Poellot, David R. Novak, Timothy J. Lang, Rachael Kroodsma, Matthew McLinden, Mariko Oue, Pavlos Kollias, Matthew R. Kumjian, Steven J. Greybush, Andrew J. Heymsfield, Joseph A. Finlon, Victoria L. McDonald, and Stephen Nicholls

Abstract

The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests.

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