Chinnawat Surussavadee, Student Member, IEEE and David H. Staelin, Fellow, IEEEStatistical Agreement • August 29th, 2000
Abstract—Accurate global observation of precipitation for all latitudes and seasons, and for all land, sea, and ice surfaces remains an elusive goal. One major obstacle to progress is lack of reliable global ground truth on horizontal and vertical scales consistent with those observed by satellite. For example, radar and scatter-dependent radiometric techniques respond to precipitation aloft, not on the ground, and can be unduly sensitive to large hydrometeors. On the other hand, rain gauges are excessively sensitive to local rainfall inhomogeneities and wind, and well-instrumented sites are rare. This paper quantitatively compares satellite observations with mesoscale numerical weather predictions of microwave brightness temperatures at those millimeter wavelengths most sensitive to hydrometeor structures and to the physics of precipitation. Only such models with high horizontal and vertical resolution allow comparisons of different sensors in detail. A Mesoscale Model, MM5, and a for
Statistical AgreementStatistical Agreement • April 25th, 2018
This statistical plan covers the First part (Part A) of the study only. The second part (Part B) of the study will be covered in a separate SAP.
Appendix 1: Statistical agreement between forest function and ecosystem service maps (Table A1.1.) and mentioned CICES divisions of ecosystem services and corresponding forest functions (Table A1.2.)Statistical Agreement • June 29th, 2017
Table A1.1. Agreement of forest functions and ecosystem services maps; true positive (TP), true negative (TN), false positive (FP), positive predictive value (PPV), negative predictive value (NPV); sensitivity (agreement of pixels classified as ESS or forest function) and specificity (agreement of pixels not classified as ESS or forest function) are scaled between 0 and 1. If an ESS/forest function is not indicated, it will not have been mentioned by the table group.
