An 8-yr climatology of storms producing large hail is estimated from satellite measurements using Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). This allows a unique, consistent comparison between regions that cannot be consistently compared using ground-based records because of varying data collection standards. Severe hailstorms are indicated most often in a broad region of northern Argentina and southern Paraguay and a smaller region in Bangladesh and eastern India. Numerous hailstorms are also estimated in the central and southeastern United States, northern Pakistan and northwestern India, central and western Africa, and southeastern Africa (and adjacent waters). Fewer hailstorms are estimated for other regions over land and scattered across subtropical oceans. Very few are estimated in the deep tropics other than in Africa. Most continental regions show seasonality with hailstorms peaking in late spring or summer. The South Asian monsoon alters the hailstorm climatology around the Indian subcontinent. About 75% of the hailstorms on the eastern side (around Bangladesh) occur from April through June, generally before monsoon onset. Activity shifts northwest to northern India in late June and July. An arc along the foothills in northern Pakistan becomes particularly active from mid-June through mid-August. The AMSR-E measurements are limited to early afternoon and late night. Tropical Rainfall Measuring Mission (TRMM) measurements are used to investigate diurnal variability in the tropics and subtropics. All of the prominent regions have hailstorm peaks in late afternoon and early evening. The United States and central Africa have the fewest overnight and early morning storms, while subtropical South America and Bangladesh have the most.
The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm 3 cm −3 . These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve.
The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.