Variational analysis of total water using AMSU

This article documents a case study comparing radiosonde humidity data to Advanced Microwave Sounding Unit (AMSU) satellite humidity data. The study had two goals: first, to develop a robust method for such a comparison, and second, to check the quality and mutual consistency of radiosonde data, radiative transfer model, and AMSU data. The radiosonde data used are Vaisala RS80 data from the station Lindenberg of the German Weather Service (DWD), which have been subject to several corrections compared to the standard data processing. The radiative transfer model is the Atmospheric Radiative Transfer Simulator (ARTS), and the AMSU data are those of the satellites NOAA 15 and 16 for the time periods 2001 and 2002. The comparison was done in radiance space, using a radiative transfer model to simulate AMSU radiances from the radiosonde data. The overall agreement is very good, with radiance biases below 1.5 K and standard deviations below 2 K. The main source of “noise” in the comparison is atmospheric inhomogeneity on the 10‐km scale. While the radiosonde correction performed at Lindenberg significantly reduces the bias between simulated and measured AMSU radiance, there still remains a slope in the radiance difference. Possible reasons for this were investigated. Most likely, the radiosondes underestimate the relative humidity under extremely dry conditions, showing 0 %RH when the true value is 2–4 %RH.

Radiative transfer simulations are performed to determine how water vapor and nonprecipitating cloud liquid water and ice particles within typical midlatitude atmospheres affect brightness temperatures TB's of moisture sounding channels used in the Advanced Microwave Sounding Unit (AMSU) and AMSU-like instruments. The purpose is to promote a general understanding of passive top-of-atmosphere TB's for window frequencies at 23.8, 89.0, and 157.0 GHz, and water vapor frequencies at 176.31, 180.3 1, and 182.31 GHz by documenting specific examples. This is accomplished through detailed analyses of TB's for idealized atmospheres, mostly representing temperate conditions over land. Cloud effects are considered in terms of five basic properties: droplet size distribution, phase, liquid or ice water content, altitude, and thickness. Effects on TB of changing surface emissivity also are addressed. The brightness temperature contribution functions are presented as an aid to physically interpreting AMSU TB's. Both liquid and ice clouds impact the TB's in a variety of ways. The TB's at 23.8 and 89 GHZ are more strongly affected by altostratus liquid clouds than by cirrus clouds for equivalent water paths. In contrast, channels near 157 and 183 GHz are more strongly affected by ice clouds. Higher clouds have a water impact on 157- and 183-GHz TB's than do lower clouds. Clouds depress TB's of the higher-frequency channels by suppressing, but not necessarily obscuring, radiance contributions from below. Thus, TB's are less closely associated with cloud-top temperatures than are IR radiometric temperatures. Water vapor alone accounts for up to 89% of the total attenuation by a midtropospheric liquid cloud for channels near 183 GHz. The Rayleigh approximation is found to be adequate for typical droplet size distributions; however, Mie scattering effects from liquid droplets become important for droplet size distribution functions with modal radii greater than 20 µm near 157 and 183 GHz, and greater than 30–40 µm at 89 GHz. This is due mainly to the relatively small concentrations of droplets much larger than the mode radius. Orographic clouds and tropical cumuli have been observed to contain droplet size distributions with mode radii in the 30–40-µm range. Thus, as new instruments bridge the gap between microwave and infrared to frequencies even higher than 183 GHz, radiative transfer modelers are cautioned to explicitly address scattering characteristics of such clouds.

In the framework of the Mesoscale Frontal Dynamics Project/FRONTS 87, a cyclone over the North Atlantic Ocean is studied with the help of the Special Sensor Microwave/Imager (SSM/I) on board the satellite of the Defense Meteorological Satellite Program. After a brief recall of the relevant studies on the frontal system of 11 November 1987 in the context of the second intensive observing period of Fronts 87, the SSM/I images are analysed to show the potential of microwave observations for diagnosing the mesoscale structure of the front. Then, considering the poor accuracy of the SSM/I regression-type algorithms in estimating the integrated cloud-liquid-water contents, we propose to retrieve this quantity with the help of a radiative-transfer model, coupled with a realistic first guess of the atmospheric characteristics in the meteorological area of concern. A physically consistent estimation is deduced from a nonlinear iterative inversion procedure, which aims at reducing the difference between simulated and measured brightness temperatures. The mesoscale structures of the retrieved fields of water vapour, cloud liquid water and rain rate appear consistent with the Fronts 87 meteorological analysis of this situation, and they complement the understanding of the processes involved in the dynamics of the front.

Total precipitable water (TPW), the amount of water vapor in a column from the surface of the earth to space, is used by forecasters to predict heavy precipitation. In this paper, a process for blending TPW values retrieved from two satellite sources is detailed: the Advanced Microwave Sounding Unit (AMSU) instruments on three NOAA satellites, and the Special Sensor Microwave Imager (SSM/I) instruments on three Defense Meteorological Satellite Program (DMSP) satellites. The process starts with a blending algorithm, which matches the cumulative probability distribution functions of TPW retrievals from the two instruments to lessen their differences. The data are then mapped to a map projection useful to forecasters and composited for 12 h to make a global map. These maps are produced hourly using Data Processing and Error Analysis System (DPEAS) software and made available to forecasters online.

The microwave radiometers on altimeter missions are specified to provide the wet troposphere path delay with an uncertainty of 1 cm or lower, at the location of the altimeter footprint. The constraints on the calibration and stability of these instruments are therefore particularly stringent. The paper addresses the questions of long-term stability and absolute calibration of the National Aeronautics and Space Administration Topography Experiment (TOPEX) and European Space Agency European Remote Sensing 2 (ERS-2) radiometers over the entire range of brightness temperatures. Selecting the coldest measurements over from the two radiometers, the drift of the TOPEX radiometer 18-GHz channel is confirmed to be about 0.2 K/year over the seven first years of the mission, and the one of the ERS-2 radiometer 23.8-GHz channel to be -0.2 K/year. The good stability of the other channels is confirmed (drift less than 0.04 K/year). The use of continental targets for analyzing the long-term drift is evaluated: the natural interannual variability prevents one from directly monitoring the drift of each channel, but the relative variation between two channels of the same instrument is found reliable. Over areas (Antarctic and Greenland plateau), results are consistent with the cold ocean analysis. Intercomparison of radiometer absolute calibrations is performed over the same continental area, leading to an anomalously high difference between channels 36.5 and 37 GHz of the ERS-2 and TOPEX radiometers, respectively, over hot targets (Sahara desert and Amazon forest). To quantify and analyze this difference, other radiometer measurements are analyzed over the Amazon forest, from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU). Biases are confirmed for both TOPEX and ERS-2 radiometers by comparing brightness temperatures and derived surface emissivities: the TOPEX radiometer channels exhibit a negative bias with respect to SSM/I and AMSU-A, whereas the ERS-2 radiometer 36.5-GHz channel is positively biased, by several kelvin in brightness temperature in both cases. The method presented here could be used for controlling the in-flight calibration of any radiometer, and correct for remaining calibration errors after launch.

For more than 30 years, satellite altimetry has been providing a unique data record, crucial for many applications such as ocean circulation and the monitoring of sea level rise at global and regional scale. The stringent requirements of such applications set the need for continuous improvement of satellite altimeter products, including those of past missions, in order to get consistent and homogeneous datasets.The accurate retrieval of sea surface heights from satellite altimetry requires the measured ranges to be corrected for a number of atmospheric and sea state effects. Amongst these effects, the path delay induced by the water vapor and cloud liquid water, the wet tropospheric correction (WTC), is still a major source of uncertainty, in particular in the long-term sea level trend.Sponsored by the European Space Agency, the FDR4ALT project aims at generating improved, up-to-date altimetric records for the historical multidisciplinary 35-day repeat missions, ERS-1, ERS-2 and Envisat. To achieve this goal, a new set of wet tropospheric corrections based on the GNSS-derived Path Delay Plus (GPD+) algorithm, has been developed for these missions and is the focus of this study. GPD+ are wet path delays, obtained by data combination through objective analysis, using all available WTC sources, including onboard radiometers, GNSS, external imaging radiometers and an atmospheric model.Since the GPD+ WTC are combined values, the intercalibration of the various datasets is a crucial step. To this end, a novel method to intercalibrate the various WTC datasets against one of the current best radiometric references, the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), hereafter designated SSMIS, has been developed.The sensor calibration consists of the adjustment of each dataset to SSMIS using a time-varying set of two parameters (2P), offset and scale factor. For this purpose, 5°x5° grids of WTC mean values for the period of each altimeter cycle and each month, respectively, are computed for SSMIS. Similar grids of mean WTC values for the period of interest are computed for each non-SSMIS sensor, including the MWR onboard each altimeter mission. Using the set of corresponding grid cells for each pair (non-SSMIS, SSMIS) the 2P of the adjustment of each sensor to the SSMIS dataset has been computed. Finally, for the whole study period (1991-2012), ERA5 model and the GNSS dataset have also been adjusted to SSMIS by means of a similar 2P model, with yearly parameters.This paper summarizes the results, focusing on the impact of the inter-calibration and the added value if these corrections for the FDR4ALT products.The new calibration revealed to be robust, less sensitive to sampling issues and able to intercalibrate any set of sensors, even with different orbits and sampling patterns. Results show that the new GPD+ WTC are continuous, consistent and intercalibrated datasets, valid over all surface types. In addition to open ocean, they also cover conditions where the measurements from the onboard microwave radiometers are invalid, mostly coastal and polar regions, recovering, on average, 25-30% of the ocean points.

The simulations of five midlatitude precipitating events by the nonhydrostatic mesoscale model Méso-NH are analyzed. These cases cover contrasted precipitation situations from 30° to 60°N, which are typical of midlatitudes. They include a frontal case with light precipitation over the Rhine River area (10 February 2000), a long-lasting precipitation event at Hoek van Holland, Netherlands (19 September 2001), a moderate rain case over the Elbe (12 August 2002), an intense rain case over Algiers (10 November 2001), and the “millennium storm” in the United Kingdom (30 October 2000). The physically consistent hydrometeor and thermodynamic outputs are used to generate a database for cloud and precipitation retrievals. The hydrometeor vertical profiles that were generated vary mostly with the 0°C isotherm, located between 1 and 3 km in height depending on the case. The characteristics of this midlatitude database are complementary to the GPROF database, which mostly concentrates on tropical situations. The realism of the simulations is evaluated against satellite observations by comparing synthetic brightness temperatures (BTs) with Advanced Microwave Sounding Unit (AMSU), Special Sensor Microwave Imager (SSM/I), and Meteosat observations. The good reproduction of the BT distributions by the model is exploited by calculating categorical scores for verification purposes. The comparison with 3-hourly Meteosat observations demonstrates the ability of the model to forecast the time evolution of the cloud cover, the latter being better predicted for the stratiform cases than for others. The comparison with AMSU-B measurements shows the skill of the model to predict rainfall at the correct location.

Results are presented of calibration/validation studies showing the ability of the Special Sensor Microwave/Imager (SSM/I) to measure total precipitable water in the atmosphere over the ocean. Comparisons between radiosondes and the SSM/I are presented for three different algorithms. The results show the possibility of a distinct improvement in the retrieval of total precipitable water over the ocean. The global, nonlinear algorithm is more sensitive to cloud liquid water content, rain, and sea ice. The additional sensitivity is due to the screening of rain and sea ice from the dependent data and the squared term in the retrieval algorithm. Thus, it will be very important to have good screening procedures for identifying these conditions. The linear algorithm overestimates in the mid-range and underestimates at large values of total precipitable water. The explanation for this effect is probably related to the selection of the center of the water vapor line as the operating frequency of the SSM/I water vapor channel. The line center is most likely to exhibit a saturation effect at large water vapor amounts, and pressure and temperature effects can also be important, depending on the distribution of water vapor in the atmosphere.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program F-16 satellite measures the Earth-emitted radiation at frequencies from 19 to 183 GHz. Compared with the Special Sensor Microwave/Imager (SSM/I), SSMIS has similar imaging channels except for two at 85.5 GHz replaced by the 91.655-GHz frequency. After the Naval Research Laboratory calibration of SSMIS imager channels, the temperature data record can be utilized operationally to derive both atmospheric and surface parameters. In this paper, several products are developed from the SSM/I heritage algorithms, including total precipitable water (TPW), cloud liquid water path (LWP), snow cover, sea ice cover, rain rate, and land surface temperature (LST). Some new products are also derived from the SSMIS, such as land emissivity. The retrieved products from F-15 SSM/I and F-16 SSMIS are intercompared to quantify the mean bias and standard deviation. It is found that because of both the relatively small mean bias and standard deviation, the F-16 SSMIS products, such as TPW, cloud LWP, snow, and sea ice, may replace the SSM/I products for operational use. However, discrepancies remain in the global rainfall estimates, LST, and land emissivity produced by each sensor. This is likely due to the imperfect F-16 SSM/I-like channels to F-15 SSM/I channels' linear mapping, particularly for 91.655-GHz channels, whose frequency is shifted from 85.5 GHz in SSM/I.

Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation experiments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the assimilation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions.

Over oceans, total column water vapour (TCWV) estimates derived from brightness temperatures of the Special Sensor Microwave Imager (SSM/I) have been shown to give accuracies similar to radiosonde measurements. Data provided by the SSM/I on the DMSP satellites are not available in real‐time, and so they are not assimilated into the ECMWF model. They can thus be used for independent validation of this important aspect of the model's hydrological cycle. The quality of ECMWF operational analyses of humidity is assessed for the period between August 1992 and November 1994. At that time, assimilation was performed through an Optimal Interpolation scheme. For this work, 2‐week averages of TCWV have been computed from analysis humidity fields of the ECMWF model and compared with simultaneous SSM/I estimates. Discrepancies between model and SSM/I estimates associated with different meteorological regimes are discussed. According to the season, the underestimation of analysed humidity in the tropics can reach 30 to 50%, whereas oceanic subsidence areas located off western coasts of subtropical continents appear too wet by as much as 60%. Mid‐latitudes are generally too moist, but to a lesser extent. An example is also shown of the utilization of SSM/I TCWV to evaluate the impact of an experimental prognostic cloud scheme on analyses and forecasts.

This study developed a physical algorithm for retrieving land surface temperatures (LST) by using the special sensor microwave imager (SSM/I) brightness temperature measurements at 19.35 and 22.23 GHz. A nonlinear algebraic equation is obtained for the LST and is solved through the Newtonian iterative procedure. The technique does not require any surface classification because the surface emissivity variations are essentially eliminated by using two closely spaced channels. In addition, the algorithm is less influenced by ice scattering from precipitation and snow cover because of the use of low frequencies. The retrieval accuracy is first examined using simulated measurements. It is found that the root‐mean‐square (RMS) error is about 3.8 K, with the largest errors occurring in dry atmospheres having total precipitable water of 5.0 mm or less. The RMS error slightly increases to 4.4 K when the algorithm is applied for the actual SSM/I measurements. Large biases were found for cold temperatures where the atmospheric water vapor content may be very low.

As part of a calibration/validation effort for the Special Sensor Microwave/Imager (SSM/I), coincident observations of SSM/I brightness temperatures and surface-based observations of cloud liquid water were obtained. These observations were used to validate initial algorithms and to derive an improved algorithm. The initial algorithms were divided into latitudinal-, seasonal-, and surface-type zones. It was found that these initial algorithms, which were of the D-matrix type, did not yield sufficiently accurate results. The surface-based measurements of channels were investigated; however, the 85 V channel was excluded because of excessive noise. It was found that there is no significant correlation between the SSM/I brightness temperatures and the surface-based cloud liquid water determination when the background surface is land or snow. A high correlation was found between brightness temperatures and ground-based measurements over the ocean.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Mesoscale cyclones in cold airstreams poleward of major frontal zones are generated frequently over the Southern Ocean. Quantitative information regarding the atmospheric water and wind fields of these systems can be retrieved from satellite‐borne microwave instruments. These instruments include the Special Sensor Microwave Imager (SSM/I), the first European Remote Sensing satellite (ERS‐1) scatterometer and the TIROS‐N operational vertical sounder (TOVS). In this study, we summarize the structures found in the SSM/I‐derived integrated water vapor (IWV), cloud liquid water, wind speed, and liquid and solid precipitation fields for 33 Southern Ocean mesolows. We present the ERS‐1 scatterometer surface wind vector fields for nine of these mesolows, and we analyze the TOVS‐derived geopotential thickness fields for one case. This study confirms earlier results and also examines several features not previously investigated in Southern Ocean mesolows. We find that (1) solid precipitation is more prevalent than liquid precipitation in most mesocyclones; (2) the higher‐latitude, smaller‐scale mesolows with spiral‐shaped cloud systems have lower IWV content than comma‐shaped mesolows; and (3) the surface cyclonic circulation of the mesolows is confirmed by the ERS‐1 winds. Surface fluxes of heat and moisture were estimated from European Centre for Medium‐Range Weather Forecasting (ECMWF) gridded fields for two highlighted cases and found to be moderate during the formation of the mesolow that occurred near the Antarctic ice edge and low for a comma cloud case. In addition, TOVS‐derived thickness patterns show that the Antarctic case lacked strong baroclinic structure, whereas comma cloud cases tend to develop in baroclinic environments.

Emission techniques are generally useful over the majority of the Earth’s surface. Low-frequency channels are better suited to measure the emission due to liquid associated with rain, most techniques to date rely on high-frequency, scattering-based schemes. The passive microwave satellite based data used includes the Special Sensor Microwave Imagers, SSMI Sounder, SAPHIR (Sondeur Atmosph rique du Profil d Humidit Intertropicale par Radiom trie), Advanced Microwave Sounding Units (AMSU), and Microwave Humidity Sounder (MHS), along with land surface model emissivity estimates. The analysis of SAPHIR sounder data brightness temperature data relating to selected vegetated land with different surface obtained for continuous period of 3 years from 2014 to 2016 was achieved. From our study SAPHIR sounder data is found useful for retrieving surface emissivity. This study relates 183.31 ± 11.0 GHz emissivity values, retrieved from a radiative transfer model using collocated SAPHIR sounder brightness temperature measurements. This examination is noteworthy for microwave sounder assimilation in climate conjecture models and for the usage of the information from passive microwave sensors on-board the Indo-French satellite “Megha-Tropiques,” which is committed to tropical environmental studies.