No abstract

The Soil Moisture Active-Passive (SMAP) L-band microwave radiometer is a conical scanning instrument designed to measure soil moisture with 4% volumetric accuracy at 40-km spatial resolution. SMAP is NASA's first Earth Systematic Mission developed in response to its first Earth science decadal survey. Here, the design is reviewed and the results of its first year on orbit are presented. Unique features of the radiometer include a large 6-m rotating reflector, fully polarimetric radiometer receiver with internal calibration, and radio-frequency interference detection and filtering hardware. The radiometer electronics are thermally controlled to achieve good radiometric stability. Analyses of on-orbit results indicate that the electrical and thermal characteristics of the electronics and internal calibration sources are very stable and promote excellent gain stability. Radiometer NEDT < 1 K for 17-ms samples. The gain spectrum exhibits low noise at frequencies >1 MHz and 1/f noise rising at longer time scales fully captured by the Piepmeier et al.

Baseline lengths and geocentric radii have been determined from GPS data without the use of fiducial sites. Data from the first GPS experiment for the IERS and Geodynamics (GIG '91) have been analyzed with a no‐fiducial strategy. A baseline length daily repeatability of 2 mm + 4 parts per billion was obtained for baselines in the northern hemisphere. Comparison of baseline lengths from GPS and the global VLBI solution GLB659 (Caprette et al. 1990) show rms agreement of 2.1 parts per billion. The geocentric radius mean daily repeatability for all sites was 15 cm. Comparison of geocentric radii from GPS and SV5 (Murray et al. 1990) show rms agreement of 3.8 cm. Given n globally distributed stations, the n(n ‐ 1)/2 baseline lengths and n geocentric radii uniquely define a rigid closed polyhedron with a well‐defined center of mass. Geodetic information can be obtained by examining the structure of the polyhedron and its change with time.

No abstract

The ability to predict short‐term variations in the Earth's rotation has gained importance in recent years owing to more precise spacecraft tracking requirements. Universal time (UT1), that component of the Earth's orientation corresponding to the rotation angle, can be measured by a number of high‐precision space geodetic techniques. A Kalman filter developed at the Jet Propulsion Laboratory (JPL) optimally combines these different data sets and generates a smoothed time series and a set of predictions for UT1, as well as for additional Earth orientation components. These UT1 predictions utilize an empirically derived random walk stochastic model for the length of the day (LOD) and require frequent and up‐to‐date measurements of either UT1 or LOD to keep errors from quickly accumulating. Recent studies have shown that LOD variations are correlated with changes in the Earth's axial atmospheric angular momentum (AAM) over timescales of several years down to as little as 8 days. AAM estimates and forecasts out to 10 days are routinely available from meteorological analysis centers; these data can supplement geodetic measurements to improve the short‐term prediction of LOD and have therefore been incorporated as independent data types in the JPL Kaiman filter. We find that AAM and, to a lesser extent, AAM forecast data are extremely helpful in generating accurate near‐real‐time estimates of UT1 and LOD and in improving short‐term predictions of these quantities out to about 10 days.