An intercalibrated Fundamental Climate Data Record (FCDR) of brightness temperatures (Tb) has been developed using data from a total of 14 research and operational conical-scanning microwave imagers. This dataset provides a consistent 30+ year data record of global observations that is well suited for retrieving estimates of precipitation, total precipitable water, cloud liquid water, ocean surface wind speed, sea ice extent and concentration, snow cover, soil moisture, and land surface emissivity. An initial FCDR was developed for a series of ten Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments on board the Defense Meteorological Satellite Program spacecraft. An updated version of this dataset, including additional NASA and Japanese sensors, has been developed as part of the Global Precipitation Measurement (GPM) mission. The FCDR development efforts involved quality control of the original data, geolocation corrections, calibration corrections to account for cross-track and time-dependent calibration errors, and intercalibration to ensure consistency with the calibration reference. Both the initial SSMI(S) and subsequent GPM Level 1C FCDR datasets are documented, updated in near real-time, and publicly distributed.

We present a comprehensive sensitivity analysis and geophysical retrieval product demonstration to assess the enhanced information content in atmospheric temperature and water vapor, harnessed in hyperspectral microwave measurements. A particular focus of this study is devoted to quantifying and comparing the impact on retrieval performance resulting from novel spectral bands of the microwave thermal spectrum, by means of data addition and data denial trade studies. Various spectral configurations are assessed, each reflecting specific technology solutions intended to maximize geophysical product performance within feasible size, weight, power and cost constraints. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to ∼40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the window regions, leading to an overall improvement of up to ∼50% in the Earth's Planetary Boundary Layer thermodynamic fields. This work concludes with an overview on the state of the art in hyperspectral microwave technology and a discussion on future applications of interest to numerical weather prediction (NWP) and climate science. The work presented in this study focuses on ocean, clear-sky demonstrations. All-sky, all-surface investigations will be the focus of a follow-up study, as we advance our capability to simulate more complex scenarios and improve scene variability.

Vicarious cold calibration (VCC) for spaceborne microwave radiometers is analyzed and modified for application to conical scanning microwave imagers at frequencies from 6 to 90 GHz. The details of the algorithm are modified to account for additional frequencies and polarizations that were not included in the development of the original algorithm. The modified algorithm is shown to produce a more stable cold reference brightness temperature (TB) than the original algorithm. An analysis is performed of this updated algorithm to show the global regions that contribute to the derivation of the cold reference TB and to show which geophysical parameters contribute to the coldest TBs. The analysis suggests that water vapor variability has the largest impact on the TBs in the VCC algorithm. The modified VCC algorithm is applied to microwave imager data and is used as an intercalibration method. It is shown to agree well with other intercalibration methods, demonstrating that it is a valid and accurate method for calibration of microwave imagers.

Striping has been observed in the MetOp-A microwave humidity sounder (MHS) data since its degradation in November 2018. However, accurate striping quantification and characterization remain challenging due to the large scene dynamics observed at W-/G-bands of MHS. Here, we have developed a set of novel algorithms for striping quantification, decomposition, characterization, and simulation. Our algorithm extracts striping from the warm-load and cold-space scenes that are relatively stable. We break down the striping into two parts of thermal and $1/f$ noises, and quantify their absolute magnitude and relative ratio. We found a significant increase in striping at 157 GHz, which has more than quadrupled by October 2019 relative to its normal level. Regardless of the degradation, the ratio of thermal and $1/f$ noises remains the same. Our simulation reproduces all the characteristics of striping against observation. It is shown that $1/f$ noise generates sharp, nonperiodic stripes, while thermal noise also generates stripes but with smoother band features. The latter is due to the periodic calibration that has a chopping effect. The striping percentage, defined as the ratio of $1/f$ to total noise, shows no dependence on the scene temperature. Striping is pronounced not only in 157 GHz but also in 89 and 190 GHz with the striping percentage over 50% while lower in 183 GHz of 20%. The results provide insights for quantifying and understanding striping. Our algorithm can be applied to other radiometers and to simulate striping for evaluating its impact on data assimilation and science products.

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5–10 km, the earth incidence angle difference is 0–0.4°, and the average brightness temperature change is 0–2 K, but individual pixels may be up to several kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset.