Douglas Klotter

Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part II: Validation of TRMM Rainfall Products

Gauge data from a West African network of 920 stations are used to assess Tropical Rainfall Measuring Mission (TRMM) satellite and blended rainfall products for 1998. In this study, mean fields, scattergrams, and latitudinal transects for the months of May‐September and for the 5-month season are presented. Error statistics are also calculated. This study demonstrates that both the TRMM-adjusted Geostationary Observational Environmental Satellite precipitation index (AGPI) and TRMM-merged rainfall products show excellent agreement with gauge data over West Africa on monthly-to-seasonal timescales and 2.5 83 2.58 latitude/longitude space scales. The root-mean-square error of both is on the order of 0.6 mm day 21 at seasonal resolution and 1 mm day21 at monthly resolution. The bias of the AGPI is only 0.2 mm day21, whereas the TRMM-merged product shows no bias over West Africa. Performance at 1.0 83 1.08 latitude/longitude resolution is also excellent at the seasonal scale and good for the monthly scale. A comparison with standard rainfall products that predate TRMM shows that AGPI and the TRMM-merged product perform as well as, or better than, those products. The AGPI shows marked improvement when compared with the GPI, in reducing the bias and in the scatter of the estimates. The TRMM satellite-only products from the precipitation radar and the TRMM Microwave Imager do not perform well over West Africa. Both tend to overestimate gauge measurements.

Validation of TRMM and Other Rainfall Estimates with a High-Density Gauge Dataset for West Africa. Part I: Validation of GPCC Rainfall Product and Pre-TRMM Satellite and Blended Products

Gauge data over North Africa are used to provide an assessment of satellite and blended rainfall products for 1988‐94 and for 1998. A comparison is also made with the Global Precipitation Climatology Center (GPCC) gauge dataset. For the 1988‐94 period, mean fields and latitudinal transects for the June‐July‐August season are presented, based on a 515-station gauge dataset, the GPCC gauge data, the Global Precipitation Climatology Project (GPCP) blended data, the infrared-based Geostationary Operational Environmental Satellite precipitation index (GPI), and the Special Sensor Microwave Imager (SSM/I) microwave estimates. Error calculations are also presented. The mean fields derived from the dense gauge network, the GPCC gauge-only analysis, and the GPCP are remarkably similar. The bias, with reference to the seasonal rainfall field based on the denser network, is about 3%‐4% for either GPCC or GPCP. Agreement is relatively good, even in individual years. The rms error associated with these datasets is 12% for seasonal rainfall totals; thus, the error is largely random. In contrast, there are large systematic errors in the satellite-only analyses of GPI and SSM/I, with biases of 20% and 40% for the mean rain field as a whole and much larger biases in individual years. The rms errors are nearly 2 times as great. For 1998, a 920-station gauge dataset was available for a smaller section of West Africa. The comparison confirmed the superior performance of GPCP and demonstrated the lower level of performance of both GPCP and GPCC at the monthly scale as compared with the seasonal scale. Overall, the results of this study underscore the continued need for extensive gauge networks to describe adequately the large-scale precipitation field over Africa.

A Two-Century Precipitation Dataset for the Continent of Africa

A wealth of historical information on climate and weather exists for the African continent. Documentary information, hydrologic indicators, and rain gauge records have been compiled and combined into a semiquantitative precipitation dataset that extends from 1801 to 1900. That dataset describes “wetness” for 90 regions of Africa, using a seven-category index. A regional gauge dataset for 1901–2000 has been converted to the seven-class system, extending coverage to two centuries. These datasets are available through the Paleoclimate Data Center.

Mesoscale Convective Systems over Western Equatorial Africa and Their Relationship to Large-Scale Circulation

Abstract This study examines mesoscale convective systems (MCSs) over western equatorial Africa using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. This region experiences some of the world’s most intense thunderstorms and highest lightning frequency, but has low rainfall relative to other equatorial regions. The analyses of MCS activity include the frequency of occurrence, diurnal and annual cycles, and associated volumetric and convective rainfall. Also evaluated is the lightning activity associated with the MCSs. Emphasis is placed on the diurnal cycle and on the continental-scale motion fields in this region. The diurnal cycle shows a maximum in MCS count around 1500–1800 LT, a morning minimum, and substantial activity during the night; there is little seasonal variation in the diurnal cycle, suggesting stationary influences such as orography. Our analysis shows four maxima in MCS activity, three of which are related to local geography (two orographic and one over Lake Victoria)....

Application of knowledge-based network processing to automated gas chromatography data interpretation

A method of translating a two-way table of qualified symptom/cause relationships into a four layer expert network for diagnosis of machine or sample preparation failure for gas chromatography is presented. This method has proven to successfully capture an experts ability to predict causes of failure in a gas chromatograph based on a small set of symptoms, derived from a chromatogram, in spite of poorly defined category delineations and definitions. In addition, the resulting network possesses the advantages inherent in most neural networks: the ability to function correctly in the presence of missing or uncertain inputs and the ability to improve performance through data-based training procedures. Acquisition of knowledge from the domain experts produced a group of imprecise cause-to-symptom relationships. These are reproduced as parallel pathways composed of symptom-filter-combination-cause node chains in the network representation. Each symptom signal is passed through a filter node to determine if the signal should be interpreted as positive or negative evidence and then modified according to the relationship established by the domain experts. The signals from several processed symptoms are then combined in the combination node(s) for a given cause. The resulting value is passed to the cause node and the highest valued cause node is then selected as the most probable cause of failure.

Corrigendum: The Centennial Trends Greater Horn of Africa precipitation dataset

This corrects the article DOI: 10.1038/sdata.2015.50.

New Rainfall Datasets for the Congo Basin and Surrounding Regions

AbstractThis paper describes three new rainfall datasets that have been developed for equatorial Africa. The development relies on acquisition of recent gauge data from the relevant countries and s...

ReverbaProp: simultaneous learning of credit and weight

We introduce a supervised learning method for feed-forward networks that solves the credit assignment problem for error in concert with solving the error reduction problem normally associated with methods such as backpropagation. The method reverberates between forward and reverse activations of the network. Forward activation using an exemplar computes output for each node in the network using the connection weights as usual. Reverse activation using output error as input computes local error at each node using reverse weights, or responsibilities, on the reverse connections. Reverse-reverse activation (the same as forward activation with linear output functions) using reverse output error as input computes local reverse error at each node. Once local error and local reverse error have been assigned to each node, weights and responsibilities are modified using the standard delta rule and local error and local reverse error, respectively. The method relies on convergence toward an optimal set of responsibilities for reverse error distribution in concert with convergence toward an optimal set of weights, and thus avoids calculation of nonlinear terms in the usual error backpropagation method. Thus the method is free of derivative evaluations, and by allowing credit assignment to optimize simultaneously with error reduction, it promotes clustering of responsibility among the nodes.