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Thomas Jung
,
Eberhard Ruprecht
, and
Friedrich Wagner

Abstract

A neural network (NN) has been developed in order to retrieve the cloud liquid water path (LWP) over the oceans from Special Sensor Microwave/Imager (SSM/I) data. The retrieval with NNs depends crucially on the SSM/I channels used as input and the number of hidden neurons—that is, the NN architecture. Three different combinations of the seven SSM/I channels have been tested. For all three methods an NN with five hidden neurons yields the best results. The NN-based LWP algorithms for SSM/I observations are intercompared with a standard regression algorithm. The calibration and validation of the retrieval algorithms are based on 2060 radiosonde observations over the global ocean. For each radiosonde profile the LWP is parameterized and the brightness temperatures (Tb’s) are simulated using a radiative transfer model.

The best LWP algorithm (all SSM/I channels except T85V) shows a theoretical error of 0.009 kg m−2 for LWPs up to 2.8 kg m−2 and theoretical “clear-sky noise” (0.002 kg m−2), which has been reduced relative to the regression algorithm (0.031 kg m−2). Additionally, this new algorithm avoids the estimate of negative LWPs.

An indirect validation and intercomparison is presented that is based upon SSM/I measurements (F-10) under clear-sky conditions, classified with independent IR-Meteosat data. The NN-based algorithms outperform the regression algorithm. The best LWP algorithm shows a clear-sky standard deviation of 0.006 kg m−2, a bias of 0.001 kg m−2, nonnegative LWPs, and no correlation with total precipitable water. The estimated accuracy for SSM/I observations and two of the proposed new LWP algorithms is 0.023 kg m−2 for LWP ⩽ 0.5 kg m−2.

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James B. Elsner
,
Tyler Fricker
,
Holly M. Widen
,
Carla M. Castillo
,
John Humphreys
,
Jihoon Jung
,
Shoumik Rahman
,
Amanda Richard
,
Thomas H. Jagger
,
Tachanat Bhatrasataponkul
,
Christian Gredzens
, and
P. Grady Dixon

Abstract

The statistical relationship between elevation roughness and tornado activity is quantified using a spatial model that controls for the effect of population on the availability of reports. Across a large portion of the central Great Plains the model shows that areas with uniform elevation tend to have more tornadoes on average than areas with variable elevation. The effect amounts to a 2.3% [(1.6%, 3.0%) = 95% credible interval] increase in the rate of a tornado occurrence per meter of decrease in elevation roughness, defined as the highest minus the lowest elevation locally. The effect remains unchanged if the model is fit to the data starting with the year 1995. The effect strengthens for the set of intense tornadoes and is stronger using an alternative definition of roughness. The elevation-roughness effect appears to be strongest over Kansas, but it is statistically significant over a broad domain that extends from Texas to South Dakota. The research is important for developing a local climatological description of tornado occurrence rates across the tornado-prone region of the Great Plains.

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