An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

Abstract

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR.

Opportunistic Constant Target Matching—A New Method for Satellite Intercalibration

“…However, the obtained biases from real observations appear reasonable in the sense that a 1 order of magnitude larger bias exists in the randomly matched data set compared to the OCTM data set, which is most likely related to the atmospheric diurnal cycle. This is in qualitative agreement with previous studies based on other methods that also did not indicate strong intersatellite bias between Metop A and NOAA 18 (Hans et al, ; John et al, ).…”

“…Intercalibrating sensors on different satellites is one of the greatest challenges in observational climate science, since global and continuous data sets spanning decades can only be constructed from multiple satellites (e.g., Hans et al, ; Mears & Wentz, ; Ohring et al, ; Shi & Bates, ; Scott et al, ; Tabata et al, ; Zou & Wang, , among many others). This article presents a new method to obtain intercalibration points for such undertakings.…”

“…We test the new method by intercomparing Channel 3 of the Microwave Humidity Sounder (MHS) on the satellites NOAA 18 and Metop A, using as reference Channel 5 of the geostationary Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the Meteosat Second Generation (MSG) satellite. This particular microwave sounder pair was chosen because a previous study (Hans et al, ) has shown them to be among the best calibrated and most stable microwave sensors, arguably agreeing within the accuracy limitations of the comparison technique. Focusing on a sensor pair with no known bias provides for a stronger test of the new method than application to a sensor pair with a large bias would.…”

Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

“…In April 2009, the zigzag pattern was the weakest, and therefore, we chose this month as the reference month for the later RFI correction. The bias seen in April 2009 is due to the antenna pattern correction (APC) of NOAA-18, which is the APC of NOAA-15 and is not suited for an MHS instrument [12]. This bias of a different origin than RFI was not considered within our RFI correction scheme (Section 4.1) but was absorbed in the reference month and, hence, was not overcorrected.…”

Homogenized Water Vapor Absorption Band Radiances From International Geostationary Satellites

“…It is important to point out that global homogenized WV radiance CDR have been generated from polar orbiting satellites, such as channel 12 of the High-Resolution Infrared Radiation Sounder (Shi & Bates, 2011) and 183 ± 1 GHz from Special Sensor Microwave Water Vapor Profiler (SSM/T-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (Hans et al, 2019). Data sets like those have been successfully used for climate studies (Buehler et al, 2008;Kottayil et al, 2016;Shi et al, 2012).…”