[1] Due to the influence of evaporation on land-surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere-Land Exchange Inverse (ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5-10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear-sky conditions when the surface is visible to the satellite sensor, often leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap-filling algorithm reproduces observed fluxes with reasonable accuracy, yielding $20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap-filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U.S. modeling domain in near real-time. A companion paper presents a climatological evaluation of ALEXI-derived evapotranspiration and moisture stress fields for the years 2002-2004.

27Given the increasing use of the term "flash drought" by the media and scientific 28 community, it is prudent to develop a consistent definition that can be used to identify 29 these events and to understand their salient characteristics. It is generally accepted that 30 flash droughts occur more often during the summer due to increased evaporative demand; 31 however, two distinct approaches have been used to identify them. The first approach 32focuses on their rate of intensification, whereas the second approach implicitly focuses on 33 their duration. These conflicting notions for what constitutes a flash drought (i.e., 34 unusually fast intensification versus short duration) introduce ambiguity that affects our 35 ability to detect their onset, monitor their development, and understand the mechanisms 36 that control their evolution. Here, we propose that the definition for flash drought should 37 explicitly focus on its rate of intensification rather than its duration, with droughts that 38 develop much more rapidly than normal identified as flash droughts. There are two 39 primary reasons for favoring the intensification approach over the duration approach. 40

Robust satellite‐derived moisture stress indices will be beneficial to operational drought monitoring, both in the United States and globally. Using thermal infrared imagery from the Geostationary Operational Environmental Satellites (GOES) and vegetation information from the Moderate Resolution Imaging Spectrometer (MODIS), a fully automated inverse model of Atmosphere‐Land Exchange (ALEXI) has been used to model daily evapotranspiration and surface moisture stress over a 10‐km resolution grid covering the continental United States. Examining monthly clear‐sky composites for April–October 2002–2004, the ALEXI evaporative stress index (ESI) shows good spatial and temporal correlation with the Palmer drought index but at considerably higher spatial resolution. The ESI also compares well to anomalies in monthly precipitation fields, demonstrating that surface moisture has an identifiable thermal signature that can be detected from space, even under dense vegetation cover. Simple empirical thermal drought indices like the vegetation health index do not account for important forcings on surface temperature, such as available energy and atmospheric conditions, and can therefore generate spurious drought detections under certain circumstances. Surface energy balance inherently incorporates these forcings, constraining ESI response in both energy‐ and water‐limited situations. The surface flux modeling techniques described here have demonstrated skill in identifying areas subject to soil moisture stress on the basis of the thermal land surface signature, without requiring information regarding antecedent rainfall. ALEXI therefore may have potential for operational drought monitoring in countries lacking well‐established precipitation measurement networks.

This study examines the evolution of several model-based and satellite-derived drought metrics sensitive to soil moisture and vegetation conditions during the extreme flash drought event that impacted major agricultural areas across the central U.S. during 2012. Standardized anomalies from the remote sensing based Evaporative Stress Index (ESI) and Vegetation Drought Response Index (VegDRI) and soil moisture anomalies from the North American Land Data Assimilation System (NLDAS) are compared to the United States Drought Monitor (USDM), surface meteorological conditions, and crop and soil moisture data compiled by the National Agricultural Statistics Service (NASS). Overall, the results show that rapid decreases in the ESI and NLDAS anomalies often preceded drought intensification in the USDM by up to 6 weeks depending on the region. Decreases in the ESI tended to occur up to several weeks before deteriorations were observed in the crop condition datasets. The NLDAS soil moisture anomalies were similar to those depicted in the NASS soil moisture datasets; however, some differences were noted in how each model responded to the changing drought conditions. The VegDRI anomalies tracked the evolution of the USDM drought depiction in regions with slow drought development, but lagged the USDM and other drought indicators when conditions were changing rapidly. Comparison to the crop condition datasets revealed that soybean conditions were most similar to ESI anomalies computed over short time periods (2-4 weeks), whereas corn conditions were more closely related to longer-range (8-12 week) ESI anomalies. Crop yield departures were consistent with the drought severity depicted by the ESI and to a lesser extent by the NLDAS and VegDRI datasets.

Deep convective storms with overshooting tops (OTs) are capable of producing hazardous weather conditions such as aviation turbulence, frequent lightning, heavy rainfall, large hail, damaging wind, and tornadoes. This paper presents a new objective infrared-only satellite OT detection method called infrared window (IRW)-texture. This method uses a combination of 1) infrared window channel brightness temperature (BT) gradients, 2) an NWP tropopause temperature forecast, and 3) OT size and BT criteria defined through analysis of 450 thunderstorm events within 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) imagery. Qualitative validation of the IRW-texture and the well-documented water vapor (WV) minus IRW BT difference (BTD) technique is performed using visible channel imagery, CloudSat Cloud Profiling Radar, and/or Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud-top height for selected cases. Quantitative validation of these two techniques is obtained though comparison with OT detections from synthetic satellite imagery derived from a cloud-resolving NWP simulation. The results show that the IRW-texture method false-alarm rate ranges from 4.2% to 38.8%, depending upon the magnitude of the overshooting and algorithm quality control settings. The results also show that this method offers a significant improvement over the WV-IRW BTD technique. A 5-yr Geosynchronous Operational Environmental Satellite (GOES)-12 OT climatology shows that OTs occur frequently over the Gulf Stream and Great Plains during the nighttime hours, which underscores the importance of using a day/night infrared-only detection algorithm. GOES-12 OT detections are compared with objective Eddy Dissipation Rate Turbulence and National Lightning Detection Network observations to show the strong relationship among OTs, aviation turbulence, and cloud-to-ground lightning activity.