Exploring the limits of variational passive microwave retrievals

2017 Summer.Includes bibliographical references.Passive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager

Sea ice-atmosphere interaction. Application of multispectral satellite data in polar surface energy flux estimates

Satellite data for the estimation of radiative and turbulent heat fluxes is becoming an increasingly important tool in large-scale studies of climate. One parameter needed in the estimation of these fluxes is surface temperature. To our knowledge, little effort has been directed to the retrieval of the sea ice surface temperature (IST) in the Arctic, an area where the first effects of a changing climate are expected to be seen. The reason is not one of methodology, but rather our limited knowledge of atmospheric temperature, humidity, and aerosol profiles, the microphysical properties of polar clouds, and the spectral characteristics of the wide variety of surface types found there. We have developed a means to correct for the atmospheric attenuation of satellite-measured clear sky brightness temperatures used in the retrieval of ice surface temperature from the split-window thermal channels of the advanced very high resolution radiometer (AVHRR) sensors on-board three of the NOAA series satellites. These corrections are specified for three different 'seasons' and as a function of satellite viewing angle, and are expected to be applicable to the perennial ice pack in the central Arctic Basin

Sea ice-atmospheric interaction: Application of multispectral satellite data in polar surface energy flux estimates

This is the third annual report on: Sea Ice-Atmosphere Interaction - Application of Multispectral Satellite Data in Polar Surface Energy Flux Estimates. The main emphasis during the past year was on: radiative flux estimates from satellite data; intercomparison of satellite and ground-based cloud amounts; radiative cloud forcing; calibration of the Advanced Very High Resolution Radiometer (AVHRR) visible channels and comparison of two satellite derived albedo data sets; and on flux modeling for leads. Major topics covered are arctic clouds and radiation; snow and ice albedo, and leads and modeling

Variability and trends of Arctic water vapour from passive microwave satellites Special role of Polar lows and Atmospheric rivers

Water in the vapour phase is the most important component of the hydrological cycle. It is formed by processes of evaporation and sublimation during which a lot of energy as latent heat is absorbed from the atmosphere. Through atmospheric large and small scale circulation, this energy is transported and released elsewhere through the process of condensation. Water vapour is the most important greenhouse gas (GHG) due to its abundance and its effectiveness in absorbing longwave radiation. In the light of global climate change, it is of great importance to identify trends of water vapour amounts in the atmosphere and its variability. Climate change in terms of the near-surface temperature is most pronounced in the Arctic, known as Arctic Amplification. Since most of the Arctic are either open ocean or sea-ice covered surfaces, only sparse ground-based observations, mostly confined to land areas are available. Therefore, one must resort to usage of the satellite based observations which offer a great advantage by their large spatial coverage. For water vapour assessment, passive microwave satellites are well suited due to their ability to sense water vapour under clear and cloudy sky conditions independent of sun light. A number of products of integrated water vapour (IWV) from various satellites are available. However, these are often inconsistent and prone to have biases due to various assumptions and uncertainties of a priori data included in the retrieval algorithms. According to the Clausius-Clapeyron relation, water vapour is constrained by the saturation vapour pressure which is constrained only by the temperature. Therefore, this thesis investigates the hypothesis that brightness temperatures (Tbs) from spaceborne passive microwave instruments can be used as a proxy for water vapour trends. To test this hypothesis, satellites based Tbs are compared to synthetic Tbs derived from the Arctic System Reanalysis (ASR). To enable the comparison, the ASR has been evaluated in Tb space by employing the Passive and Active Microwave TRAnsfer forward model (PAMTRA). Moreover, Tbs from sounding channels were correlated with corresponding IWV based on the weighted absolute humidity profiles peaks. The hypothesis is tested for the dry, cold and sun-absent winter season (January) and the sun-return transitional spring season (May). The results show that Tbs from frequency channels can explain trends in the corresponding IWV columns derived from ASR for regions with significant positive trends for both, Tb and IWV since high correlation coefficients, reaching 0.98, have been found. This is true for different time scales, daily, monthly and for the period of 17 years (2000-2016). The exception to this has been found for May for daily time scale for frequency channel dominated by the signal from the upper troposphere lower stratosphere (UTLS). For this combination of Tbs and IWV correlations tend to be weaker and at some locations even negative. This is consistent with theoretical calculations and observational studies which report a cooling in the UTLS region for increasing IWV. However, Tbs from the corresponding channel seem less reliable in explaining trends of the corresponding IWV derived from the ASR. This indicates the importance of other processes relevant in the UTLS region during spring. Furthermore, this thesis investigates synoptic features which are associated with water vapour transport and precipitation. Previous studies have shown that Arctic cyclone activity during winter has a large impact on the sea ice melt in the following seasons making them important players in the complex feedback mechanism of the climate change in the Arctic. However, the life cycle of the most intense of such cyclones, also known as polar lows (PL) are not yet fully understood. To analyse their dynamics, this thesis investigates different environmental conditions (and their combination) between genesis and maturity stage of January PLs. PLs with overall lower thermal instability between the surface and 500 hPa during formation stage are typically accompanied by higher and steeper lapse rates throughout the boundary layer. Therefore these PLs were fostering convective development. However, as observed for a few cases, a decreased thermal instability alongside a simultaneous decrease of convection coincides with high relative humidity (mostly above 90%). Furthermore, higher relative humidity at lower levels during genesis stage promoted stronger winds at the maturity stage. Besides water vapour turnover associated with Arctic cyclones, atmospheric rivers (ARs) transport major amounts of moisture from tropical and extratropical regions into the Arctic. Studies have shown that about 90% of the total mid-latitude vertically integrated water vapour transport (IVT) is related to these synoptic features. To study the influence of ARs on PL precipitation, an event with a coupled AR and PL is compared to an event which featured only a PL. The AR had a strong influence on the PL resulting in higher snow amounts on the order of ∼ 4 kg/m2 higher wind speeds and a longer distance traveled during its life cycle, compared to the PL only case

RETRIEVAL OF ICE CLOUD PARAMETERS USING DMSP SPECIAL SENSOR MICROWAVE IMAGER/SOUNDER

Clouds exert a profound influence on both the water balance of the atmosphere and the earth's radiation budget (Stephens 2005; Stephens and Kummerow 2007). Among the global distribution, 30% of them are ice clouds (Riedi et al. 2000). It is important to improve our knowledge of the ice cloud properties in order to determine their influence to the global ecosystem. For ice clouds with millimeter-size ice particles, which are generally found in anvil cirrus and deep convections, microwave and millimeter wave length satellite measurements are suitable for the ice cloud microphysical property retrieval because of its strong ability to penetrate deeper into dense ice clouds. For these types of ice clouds, brightness temperatures at the top of the atmosphere are analytically derived as a function of vertically integrated ice water content (i.e. ice water path), effective particle diameter, and bulk volume density. In general, three brightness temperature measurements are needed to retrieve the three ice cloud microphysical parameters. A two-stream radiative transfer theory was applied to data from the Advanced Microwave Sounding Unit (AMSU) and the Moisture Humidity Sensor (MHS) in order to generate global ice water paths operationally. This research further applied the model and theory to derive ice water path (IWP) from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite. Compared to AMSU/MHS, which have field of views (FOV) varying with scan position, SSMIS scans the Earth's atmosphere at a constant viewing angle of 53o and therefore offers a uniform FOV within each scan. This unique feature allows for improved global mapping and monitoring of ice clouds so that a more accurate and realistic IWP and ice particle effective diameter distribution is expected. A direct application of SSMIS-derived ice water path is its relationship with surface rain rate as derived previously for AMSU and MHS instruments. Here, SSMIS-derived rain rate was compared to the AMSU and MHS rainfall products and hourly synthetic precipitation observations from rain gauges and surface radar. Results show that SSMIS surface precipitation distribution is spatially consistent and does not have apparent artificial boundary near coastal zones as previously seen in other algorithms. Also, the ice water path associated with a severe storm reasonably delineates the strong convective precipitation areas and has a spatial variation consistent with surface precipitation. From retrieved instantaneous surface precipitation, a tropical and subtropical oceanic precipitation anomaly time series is constructed from 5 year's worth (2005-2009) of SSMIS data. This data record is also linked to the previous constructed SSM/I 15-year (1992-2006) data record to provide a longer term climate study by satellite observations. In future studies, refined algorithms for the estimate of ice cloud base temperature and ice particle bulk volume density are going to be developed to improve the accuracy of IWP retrieval under various cloud vertical distributions. Meanwhile, a better inter-sensor cross calibration scheme is the key to make satellite measurements more useful in climate change study

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

NASA sea ice and snow validation plan for the Defense Meteorological Satellite Program special sensor microwave/imager

This document addresses the task of developing and executing a plan for validating the algorithm used for initial processing of sea ice data from the Special Sensor Microwave/Imager (SSMI). The document outlines a plan for monitoring the performance of the SSMI, for validating the derived sea ice parameters, and for providing quality data products before distribution to the research community. Because of recent advances in the application of passive microwave remote sensing to snow cover on land, the validation of snow algorithms is also addressed

Small-scale structure of thermodynamic phase in Arctic mixed-phase clouds observed with airborne remote sensing during the ACLOUD campaign

This thesis evaluates the limitations of passive airborne remote sensing methods to retrieve optical and microphysical properties of Arctic mixed-phase clouds. These limitations are circumvented using a synergy of passive and active remote sensing techniques, and large eddy simulations. Using this synergetic approach, the three-dimensional spatial distribution of the thermodynamic phase of two cloud case studies is characterized. The findings are subsequently applied to a statistical analysis of the cloud properties measured during the Arctic Cloud Observations Using airborne measurements during polar Day (ACLOUD) campaign

Remote sensing of atmospheric water vapor, liquid water and wind speed at the ocean surface by passive microwave techniques from the Nimbus-5 satellite

The microwave brightness temperature measurements for Nimbus-5 electrically scanned microwave radiometer and Nimbus E microwave spectrometer are used to retrieve the atmospheric water vapor, liquid water and wind speed by a quasi-statistical retrieval technique. It is shown that the brightness temperature can be utilized to yield these parameters under various weather conditions. Observations at 19.35 GHz, 22.235 GHz and 31.4 GHz were input to the regression equations. The retrieved values of these parameters for portions of two Nimbus-5 orbits are presented. Then comparison between the retrieved parameters and the available observations on the total water vapor content and the surface wind speed are made. The estimated errors for retrieval are approximately 0.15 g/sq cm for water vapor content, 6.5 mg/sq cm for liquid water content and 6.6 m/sec for surface wind speed