Advanced microwave sounding unit study for atmospheric infrared sounder

The Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU-A), and the Microwave Humidity Sounder (MHS, formerly AMSU-B) together constitute the advanced sounding system facility for the Earth Observing System (EOS). A summary of the EOS phase B activities are presented

Relative humidity vertical profiling using lidar-based synergistic methods in the framework of the Hygra-CD campaign

Accurate continuous measurements of relative hu- midity (RH) vertical profiles in the lower troposphere have become a significant scientific challenge. In recent years a synergy of various ground-based remote sensing instru- ments have been successfully used for RH vertical profil- ing, which has resulted in the improvement of spatial reso- lution and, in some cases, of the accuracy of the measure- ment. Some studies have also suggested the use of high- resolution model simulations as input datasets into RH ver- tical profiling techniques. In this paper we apply two syn- ergetic methods for RH profiling, including the synergy of lidar with a microwave radiometer and high-resolution at- mospheric modeling. The two methods are employed for RH retrieval between 100 and 6000 m with increased spatial res- olution, based on datasets from the HygrA-CD (Hygroscopic Aerosols to Cloud Droplets) campaign conducted in Athens, Greece from May to June 2014. RH profiles from synergetic methods are then compared with those retrieved using single instruments or as simulated by high-resolution models. Our proposed technique for RH profiling provides improved sta- tistical agreement with reference to radiosoundings by 27 % when the lidar–radiometer (in comparison with radiometer measurements) approach is used and by 15 % when a lidar model is used (in comparison with WRF-model simulations). Mean uncertainty of RH due to temperature bias in RH pro- filing was ~ 4 . 34 % for the lidar–radiometer and ~ 1 . 22 % for the lidar–model methods. However, maximum uncer- tainty in RH retrievals due to temperature bias showed that lidar-model method is more reliable at heights greater than 2000 m. Overall, our results have demonstrated the capabil- ity of both combined methods for daytime measurements in heights between 100 and 6000 m when lidar–radiometer or lidar–WRF combined datasets are available.Peer ReviewedPostprint (author's final draft

Characterizing maritime trade-wind convection using the HALO Microwave Package (HAMP)

This thesis explores the marine trade-wind convection and the clouds forming within by using spatial-high-resolution airborne remote sensing observations taken from the German High Altitude and LOng range research aircraft (HALO). The nadir-pointing HALO Microwave Package (HAMP) is the central tool of this thesis. HAMP comprises a cloud radar and a 26-channel microwave radiometer (MWR, 22–183 GHz), for which the atmosphere and clouds are semitransparent. The shallow cumulus clouds, like they regularly occur in the trade-wind region, are of particular interest for better understanding the climate. Several studies (e.g., Bony and Dufresne, 2005; Schneider et al., 2017) identified such clouds as a main source of model spread in climate projections. The challenge of this kind of ubiquitous clouds in the models is partly due to large spread in global observations which can be related to the small scale of shallow cumuli and the coarse-scale observations from satellites. This thesis combines three studies around HAMP from the characterization of the HAMP MWR, over the development of MWR retrievals for liquid clouds to the application by evaluating two cloud-resolving simulations. The HAMP MWR is characterized by investigating the random noise of each channel, the covariance within each of the five frequency bands, the brightness temperature (BT) offset, the offset stability, and by suggesting an offset correction. The offset and stability of the HAMP BT acquisitions are studied by comparing the measured BTs to synthetic measurements based on forward-simulated dropsondes. Offsets between −11 and +6 K show a spectral dependency, which repeatedly appears but is shifted between flights. The offsets are most likely caused by uncertainties in the calibration method and changing environmental conditions of the MWR in the belly pod during take-off and ascending. However, an offset correction based on the dropsondes can be developed for each channel as a function of the flight. To better interpret the HAMP BT observations, novel retrievals are developed based on a realistic database of synthetic measurements and corresponding atmospheric profiles. Retrievals of the liquid water path (LWP), rainwater path (RWP), and integrated water vapor (IWV) are developed to describe the clouds and their environment. The retrieved IWV using the offset-corrected BTs agrees with coincident dropsondes and water vapor lidar measurements by 1.4 kg/m² . The theoretical assessment of LWP shows that the LWP error is below 20 g/m² for LWP below 100 g/m² . The absolute LWP error increases with increasing LWP, but the relative error decreases from 20 % at 100 g/m² to 10 % at 500 g/m². The RWP retrieval, which uses the radar in addition to the MWR, can reliably detect RWP larger than 10 g/m² with a Gilbert skill score > 0.75. The retrieval results are summarized in a comparison of the clouds and their moisture environment in the two tropical seasons, which are represented by the field experiments in December 2013 (dry season) and in August 2016 (wet season). Clouds were more frequent, and their average LWP and RWP were higher in the dry season than in the wet season. However, deeper convection with the formation of large frozen particles was less frequent in the dry season. It is hypothesized, that the lower degree of cloud organization in the dry season led to smaller systems with more overall cloud cover. The higher degree of randomness in the dry season comes along with less extremes and is reflected by a narrower distribution of IWV. The variability between (especially the wet-season) flights shows, how statistics from airborne campaigns are affected by the choice of the individual flight pattern. The more homogeneous and cloudy statistics of the dry season are used to assess the representation of shallow cumulus convection and the cloud formation over the ocean in two cloud-resolving simulations generated with the ICON model. The HAMP radar and a backscatter lidar are used for detecting cloud top height (CTH), base height, and precipitation, and the MWR stratifies the cases by LWP. Forward simulators are used to derive the same measurements synthetically from the model data while applying the same instrument-specific cloud-detection thresholds. The analysis reveals a bimodal structure of the CTH. The lower mode relates to boundary layer driven clouds, while the upper mode is driven by moist shallow convection, trapped under the trade inversion at about 2.3 km above sea level. The storm-resolving model (SRM) with 1.25 km horizontal grid spacing resolves the two cloud layers to a limited extend. Most CTHs in the SRM are above the observed lower CTH mode, and top height increases with LWP. The second model with a 300 m grid (large-eddy model, LEM) represents better the observed bimodal distribution of CTH. However, the microphysical schema of neither model can produce in-cloud drizzle-sized particles that were often observed by the radar. This application study shows, how HAMP on HALO provides insightful data to help closing the uncertainty in the models, if interpreted thoroughly

Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC

The microwave radiometers HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds - Passive) continuously measured radiation emitted from the atmosphere throughout the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition on board the research vessel Polarstern. From the measured brightness temperatures, we have retrieved atmospheric variables using statistical methods in a temporal resolution of 1 s covering October 2019 to October 2020. The integrated water vapour (IWV) is derived individually from both radiometers. In addition, we present the liquid water path (LWP), temperature and absolute humidity profiles from HATPRO. To prove the quality and to estimate uncertainty, the data sets are compared to radiosonde measurements from Polarstern. The comparison shows an extremely good agreement for IWV, with standard deviations of 0.08–0.19 kg m−2 (0.39–1.47 kg m−2) in dry (moist) situations. The derived profiles of temperature and humidity denote uncertainties of 0.7–1.8 K and 0.6–0.45 gm−3 in 0–2 km altitude

Remote sensing of water vapor over land using the advanced microwave sounding unit

Includes bibliographical references.Water vapor is a fundamentally important variable in the atmosphere for making accurate forecasts. Its global distribution is a challenge to determine and can change rapidly in both space and time. Several ground and space based methods are currently employed to determine its spatial and temporal variability. The microwave spectrum is very useful for remote sensing due to its ability to penetrate through clouds at most frequencies. Microwave satellite sensors have been used to retrieve atmospheric state parameters for several decades, however the retrievals of certain parameters have not been performed satisfactorily over land thus far. Retrievals rely on the ability to extract the atmospheric state from the upwelling radiation, most of which comes from emission from the surface. Knowing the surface emissivity to a high degree of accuracy is essential for calculating the land surface temperature, however it is also important because this emission must be removed in order to retrieve the atmospheric parameters desired. Land type, vegetation, snow, ice, rain, urbanization effects, and many other factors have an effect on the aggregate emission within each viewing scene and results in a strong sensitivity and variability of microwave emissivity on small scales. A physically based iterative optimal estimation retrieval has been implemented to retrieve atmospheric parameters from the Advanced Microwave Sounding Unit (AMSU). This retrieval is based on the method of Engelen and Stephens (1999). The retrieval uses a first guess of water vapor and temperature profiles (currently from radiosondes, but will soon be from GDAS), and uses a first guess of emissivity at each of five frequencies (from the MEM). The retrieval was run with a highly accurate first guess in order to detect bias, and the total precipitable water amounts were validated against a radiosonde match-up dataset. The match-up showed fair agreement between the radiosondes and the retrieval (within 20%), however a systematic bias was detected due mostly to coastline contamination. Data from the Global Positioning System (GPS) was also used to validate the total precipitable water, however the results showed less agreement than the radiosonde results (variations of ~20-35%). Most of this disagreement stemmed from geographical co-location differences. The analytical Jacobian was also examined to determine the sensitivities of all channels to the state vector parameters. This enables any retrieval user to pick a channel configuration that gives the desired sensitivities. Vertical profiles of water vapor sensitivities at four varying emissivities were investigated. Sensitivities of water vapor to emissivity were also examined at three distinct atmospheric pressure levels. The Jacobian determined that water vapor is able to be detected throughout a vertical column with adequate skill, although problematic areas occurred between 600 and 800 mb as the emissivity approached unity (e>0.99) for a wet atmospheric case. These results give confidence that AMSU can detect TPW over land for both weather forecasting and for climate studies. The current capabilities may be improved further once bias sources are dealt with satisfactorily.Research was supoprted in part by Cloud Sat at NASA-Goddard under Contract Agreement NAS5-99237, the DoD Center for Geosciences/Atmospheric Research at Colorado State University under the Cooperative Agreement DAAD19-02-2-0005 with the Army Research Lab, and by the Joint Center for Satellite Data Assimilation (JCSDA) Program via NOAA grant NA17RJ1228#15 under CIRA's Cooperative Agreement with NOAA

Parametric optimal estimation retrieval of the non-precipitating parameters over the global oceans, A

2006 Summer.Includes bibliographical references (pages 82-87).Covers not scanned.Print version deaccessioned 2021.There are a multitude of spacebome microwave sensors in orbit, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave/lmager (SSM/I) onboard the DMSP satellites, the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), SSMIS, WINDSAT, and others. Future missions, such as the planned Global Precipitation Measurement (GPM) Mission, will incorporate additional spacebome microwave sensors. The need for consistent geophysical parameter retrievals among an ever-increasing number of microwave sensors requires the development of a physical retrieval scheme independent of any particular sensor and flexible enough so that future microwave sensors can be added with relative ease. To this end, we attempt to develop a parametric retrieval algorithm currently applicable to the non-precipitating atmosphere with the goal of having consistent non-precipitating geophysical parameter products. An algorithm of this nature makes is easier to merge separate products, which, when combined, would allow for additional global sampling or longer time series of the retrieved global geophysical parameters for climate purposes. This algorithm is currently applied to TMI, SSM/I and AMSR-E with results that are comparable to other independent microwave retrievals of the non-precipitating parameters designed for specific sensors. The physical retrieval is developed within the optimal estimation framework. The development of the retrieval within this framework ensures that the simulated radiances corresponding to the retrieved geophysical parameters will always agree with observed radiances regardless of the sensor being used. Furthermore, a framework of this nature allows one to easily add additional physics to describe radiation propagation through raining scenes, thus allowing for the merger of cloud and precipitation retrievals, if so desired. Additionally, optimal estimation provides error estimates on the retrieval, a product often not available in other algorithms, information on potential forward model/sensor biases, and a number of useful diagnostics providing information on the validity and significance of the retrieval (such as Chi-Square, indicative of the general "fit" between the model and observations and the A-Matrix, indicating the sensitivity of the model to a change in the geophysical parameters). There is an expected global response of these diagnostics based on the scene being observed, such as in the case of a raining scene. Fortunately, since TRMM has a precipitation radar (TRMM PR) in addition to a radiometer (TMI) flying on-board, the expected response of the retrieval diagnostics to rainfall can be evaluated. It is shown that a potentially powerful rainfall screen can then be developed for use in passive microwave rainfall and cloud property retrieval algorithms with the possibility of discriminating between precipitating and nonprecipitating scenes, and further indicating the possible contamination of rainfall in cloud liquid water path microwave retrievals