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Gary J. Jedlovec, Udaysankar Nair, and Stephanie L. Haines

Abstract

The damage surveys conducted by the NWS in the aftermath of a reported tornadic event are used to document the location of the tornado ground damage track (pathlength and width) and an estimation of the tornado intensity. This study explores the possibility of using near-real-time medium and high spatial resolution satellite imagery from the NASA Earth Observing System satellites to provide additional information for the surveys. Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data were used to study the damage tracks from three tornadic storms: the La Plata, Maryland, storm of 28 April 2002 and the Ellsinore and Marquand, Missouri, storms of 24 April 2002. These storms varied in intensity and occurred over regions with significantly different land cover. It was found that, depending on the nature of the land cover, tornado damage tracks from intense storms (F1 or greater) and hail storms may be evident in ASTER, Landsat, and MODIS satellite imagery. In areas where the land cover is dominated by forests, the scar patterns can show up very clearly, while in areas of grassland and regions with few trees, scar patterns are not as obvious or cannot be seen at all in the satellite imagery. The detection of previously unidentified segments of a damage track caused by the 24 April 2002 Marquand, Missouri, tornado demonstrates the utility of satellite imagery for damage surveys. However, the capability to detect tornado tracks in satellite imagery depends on the ability to observe the ground without obstruction from space and appears to be as much dependent on the nature of the underlying surface and land cover as on the severity of the tornadic storm.

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Jatin Kala, Tom J. Lyons, Ian J. Foster, and Udaysankar S. Nair

Abstract

A two-layer steady-state resistance model is compared with routine meteorological data collected from the Western Australian wheat belt during 2000–06. Major difficulties in implementing such a model are the correct parameterization for the incoming longwave radiation and estimation of daily soil moisture, neither of which are routinely measured. These difficulties are addressed by testing parameterizations for incoming longwave radiation calibrated to local conditions and incorporating a soil–water balance model based on routine weather data. The modified model has RMSE and biases ranging from 2.4° to 3.1°C and −0.2° to 0.8°C, respectively, across the wheat belt when comparing all minimum nocturnal temperatures. The model is shown to predict frost events approximately 55% of the time and illustrates that frost damage to foliage may occur when screen temperatures are < 2°C.

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Udaysankar S. Nair, Salvi Asefi, Ronald M. Welch, D. K. Ray, Robert O. Lawton, Vani Starry Manoharan, Mark Mulligan, Tom L. Sever, Daniel Irwin, and J. Alan Pounds

Abstract

This study details two unique methods to quantify cloud-immersion statistics for tropical montane cloud forests (TMCFs). The first technique uses a new algorithm for determining cloud-base height using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, and the second method uses numerical atmospheric simulation along with geostationary satellite data. Cloud-immersion statistics are determined using MODIS data for March 2003 over the study region consisting of Costa Rica, southern Nicaragua, and northern Panama. Comparison with known locations of cloud forests in northern Costa Rica shows that the MODIS-derived cloud-immersion maps successfully identify known cloud-forest locations in the United Nations Environment Programme (UNEP) World Conservation Monitoring Centre (WCMC) database. Large connected regions of cloud immersion are observed in regions in which the trade wind flow is directly impinging upon the mountain slopes; in areas in which the flow is parallel to the slopes, a fractured spatial distribution of TMCFs is observed. Comparisons of the MODIS-derived cloud-immersion map with the model output show that the MODIS product successfully captures the important cloud-immersion patterns in the Monteverde region of Costa Rica. The areal extent of cloud immersion is at a maximum during morning hours and at a minimum during the afternoon, before increasing again in the evening. Cloud-immersion frequencies generally increase with increasing elevation and tend to be higher on the Caribbean Sea side of the mountains. This study shows that the MODIS data may be used successfully to map the biogeography of cloud forests and to quantify cloud immersion over cloud-forest locations.

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Ronald M. Welch, Salvi Asefi, Jian Zeng, Udaysankar S. Nair, Qingyuan Han, Robert O. Lawton, Deepak K. Ray, and Vani Starry Manoharan

Abstract

Cloud-base heights over tropical montane cloud forests are determined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products and National Centers for Environmental Prediction global tropospheric final analysis (FNL) fields. Cloud-base heights are computed by subtracting cloud thickness estimates from cloud-top height estimates. Cloud-top pressures determined from the current MODIS retrieval algorithm often have serious cloud-top pressure retrieval errors at pressures > 700 hPa. The problem can be easily remedied by matching cloud-top temperature derived from the 11-μm channel to the dewpoint temperature profile (instead of the temperature profile) obtained from the FNL dataset. The FNL dataset at 1° spatial resolution produced results that were nearly equivalent to those derived from radiosonde measurements. The following three different approaches for estimating cloud thickness are examined: 1) the constant liquid water method, 2) the empirical method, and 3) the adiabatic model method. The retrieval technique is applied first for stratus clouds over U.S. airports for 12 cases, with cloud-base heights compared with ceilometer measurements. Mean square errors on the order of 200 m result. Then, the approach is applied to orographic clouds over Monteverde, Costa Rica, with estimated cloud-base heights compared with those derived from photographs. Mean square errors on the order of 100 m result. Both the empirical and adiabatic model approaches produce superior results when compared with the constant liquid water (CLW) approach. This is due to the fact that CLW is more sensitive to natural variations in cloud optical thickness.

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Eric Rappin, Rezaul Mahmood, Udaysankar Nair, Roger A. Pielke Sr., William Brown, Steve Oncley, Joshua Wurman, Karen Kosiba, Aaron Kaulfus, Chris Phillips, Emilee Lachenmeier, Joseph Santanello Jr., Edward Kim, and Patricia Lawston-Parker

Abstract

Extensive expansion in irrigated agriculture has taken place over the last half century. Due to increased irrigation and resultant land use land cover change, the central United States has seen a decrease in temperature and changes in precipitation during the second half of 20th century. To investigate the impacts of widespread commencement of irrigation at the beginning of the growing season and continued irrigation throughout the summer on local and regional weather, the Great Plains Irrigation Experiment (GRAINEX) was conducted in the spring and summer of 2018 in southeastern Nebraska. GRAINEX consisted of two, 15-day intensive observation periods. Observational platforms from multiple agencies and universities were deployed to investigate the role of irrigation in surface moisture content, heat fluxes, diurnal boundary layer evolution, and local precipitation.

This article provides an overview of the data collected and an analysis of the role of irrigation in land-atmosphere interactions on time scales from the seasonal to the diurnal. The analysis shows that a clear irrigation signal was apparent during the peak growing season in mid-July. This paper shows the strong impact of irrigation on surface fluxes, near-surface temperature and humidity, as well as boundary layer growth and decay.

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Rezaul Mahmood, Roger A. Pielke Sr., Kenneth G. Hubbard, Dev Niyogi, Gordon Bonan, Peter Lawrence, Richard McNider, Clive McAlpine, Andres Etter, Samuel Gameda, Budong Qian, Andrew Carleton, Adriana Beltran-Przekurat, Thomas Chase, Arturo I. Quintanar, Jimmy O. Adegoke, Sajith Vezhapparambu, Glen Conner, Salvi Asefi, Elif Sertel, David R. Legates, Yuling Wu, Robert Hale, Oliver W. Frauenfeld, Anthony Watts, Marshall Shepherd, Chandana Mitra, Valentine G. Anantharaj, Souleymane Fall, Robert Lund, Anna Treviño, Peter Blanken, Jinyang Du, Hsin-I Chang, Ronnie Leeper, Udaysankar S. Nair, Scott Dobler, Ravinesh Deo, and Jozef Syktus
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