Community Review of Southern Ocean Satellite Data Needs

This review represents the Southern Ocean community’s satellite data needs for the coming decade. Developed through widespread engagement, and incorporating perspectives from a range of stakeholders (both research and operational), it is designed as an important community-driven strategy paper that provides the rationale and information required for future planning and investment. The Southern Ocean is vast but globally connected, and the communities that require satellite-derived data in the region are diverse. This review includes many observable variables, including sea-ice properties, sea-surface temperature, sea-surface height, atmospheric parameters, marine biology (both micro and macro) and related activities, terrestrial cryospheric connections, sea-surface salinity, and a discussion of coincident and in situ data collection. Recommendations include commitment to data continuity, increase in particular capabilities (sensor types, spatial, temporal), improvements in dissemination of data/products/uncertainties, and innovation in calibration/validation capabilities. Full recommendations are detailed by variable as well as summarized. This review provides a starting point for scientists to understand more about Southern Ocean processes and their global roles, for funders to understand the desires of the community, for commercial operators to safely conduct their activities in the Southern Ocean, and for space agencies to gain greater impact from Southern Ocean-related acquisitions and missions.The authors acknowledge the Climate at the Cryosphere program and the Southern Ocean Observing System for initiating this community effort, WCRP, SCAR, and SCOR for endorsing the effort, and CliC, SOOS, and SCAR for supporting authors’ travel for collaboration on the review. Jamie Shutler’s time on this review was funded by the European Space Agency project OceanFlux Greenhouse Gases Evolution (Contract number 4000112091/14/I-LG)

인공위성 수동 관측자료를 활용한 겨울철 북극 해빙지역 적설깊이 산출 및 장기변동성 분석

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2021.8. 손병주.A new satellite retrieval algorithm for wintertime snow depth on Arctic sea ice was developed based on the hydrostatic balance and thermodynamic steady-state of a snow-ice system. In this algorithm, snow depth is estimated from the passive infrared and microwave measurements, with the use of sea ice freeboard, snow surface temperature, and snow-ice interface temperature as inputs. The algorithm was validated against NASA's Operation IceBridge (OIB) measurements, and results indicate that the snow depth on the Arctic sea ice can be estimated with a high level of accuracy. To produce a long-term snow depth record in the Arctic basin-scale, sea ice freeboard was estimated from the satellite passive microwave (PMW) measurements. To do so, the snow-ice scattering optical depth from satellite PMW measurements was used as a predictor for the estimation of the total freeboard. Estimated PMW total freeboards were found to be in good agreement with OIB total freeboards. The wintertime snow depth records for the 2003-2020 period were produced by combining the PMW freeboard and satellite-derived temperatures. It was found that snow depth is highly dependent on sea ice type, likely due to the snow accumulation timing and period. The snow depth and its variability were greater on multiyear ice than on first-year ice. Besides, a significant reduction in mean snow depth was found, compared to the snow depth climatology for the 1954-1991 period. Regarding the temporal variations over the 2003-2020 period, regionally different snow depth trends are found; negative and positive snow depth trends were noted over the eastern and western parts of the Arctic Ocean, respectively. It is thought that the negative trends are related to sea ice type transition and delayed freeze onset, while the positive trends are related to increased precipitation amount.겨울철 북극 해빙지역 적설깊이 산출을 위해 적설-해빙 시스템의 정역학적 평형 및 열역학적 정상상태(steady state)를 기반으로 한 새로운 인공위성 산출 알고리즘이 개발되었다. 개발된 알고리즘은 수동 마이크로파/적외선 관측자료로부터 얻어진 해빙건현(freeboard), 적설표면온도와 적설-해빙경계층온도를 입력자료로 사용하여 적설깊이 산출을 수행한다. 적설깊이 산출물은 NASA의 OIB(Operation IceBridge) 항공기 관측자료를 기준으로 검증되었으며 높은 정확도를 보였다. 새로운 알고리즘을 활용한 북극해 규모의 장기간 적설깊이 자료 생산을 위해 인공위성 수동 마이크로파 관측자료로부터 해빙건현을 추정하였다. 이를 위해 수동 마이크로파 관측에서 얻은 적설-해빙 산란 광학 깊이를 예측 변수로 사용하였다. 추정된 수동 마이크로파 해빙건현은 OIB 관측치와 높은 일치성을 보였다. 2003-2020년 기간 겨울철 적설깊이 자료를 인공위성 온도자료와 수동마이크로파 해빙건현자료를 함께 사용하여 생산하고 분석하였다. 북극 해빙지역 적설깊이는 눈이 쌓이는 시기 및 기간과 관련하여 해빙의 종류에 크게 의존하는 것으로 나타났으며, 적설깊이와 그 변동성은 단년빙보다 다년빙에서 큰 값을 보였다. 또한, 1954-1991년 기간 동안의 현장관측 기반 적설깊이 기후값과 비교하여 현대 적설깊이의 상당한 감소가 발견되었다. 시계열 분석 결과 2003-2020년 기간동안 지역적으로 다른 적설깊이 경향성이 보였다. 북극해의 동쪽지역에서는 감소, 서쪽지역에서는 증가 경향이 나타났다. 감소 경향은 다년빙에서 단년빙으로의 해빙 종류 변화 및 결빙시점의 지연과 관련이 있으며, 증가 경향은 강수량의 증가와 관련이 있는 것으로 보인다.1. Introduction 1 2. Previous studies in obtaining Arctic snow depth 11 2.1. In situ measurements 11 2.1.1. Snow depth climatology 11 2.1.2. Arctic buoy programs 14 2.2. Remote sensing 17 2.2.1. Operation IceBridge 17 2.2.2. Satellite passive microwave (PMW) measurements 20 2.2.3. Dual-frequency satellite altimetry 22 3. Used data 23 3.1. Snow-ice temperature profiles 23 3.2. Satellite data 25 3.2.1. PMW brightness temperature 25 3.2.2. Snow surface temperature 28 3.2.3. Total freeboard 29 3.3. Auxiliary data 31 4. Methods 34 4.1. Algorithm development 34 4.1.1. New method using thickness ratio (TR) 34 4.1.2. Theoretical background of TR 38 4.1.3. Strategy for obtaining TR 41 4.1.4. Buoy data preprocessing 43 4.1.5. Snow depth retrieval procedure 46 4.2. Sea ice parameters from satellite PMW measurements 49 4.2.1. Simplified radiative transfer model 49 4.2.2. Snow-ice scattering optical depth 52 4.2.3. Sea ice type 55 5. Results 59 5.1. Snow depth retrieval algorithm 59 5.1.1. TR-temperature equation 59 5.1.2. Snow depth retrieval and validation 63 5.2. Long-term snow depth record 67 5.2.1. PMW total freeboard 67 5.2.2. Snow depth from satellite passive measurements 74 5.2.3. Uncertainty estimation and sensitivity test 78 5.3. Analysis of Arctic snow depth during 2003-2020 period 85 5.3.1. Geographical distribution 85 5.3.2. Temporal variation 91 6. Conclusions and discussion 97 References 106 국문 초록 120박

Consistency in the AMSR-E snow products: groundwork for a coupled snowfall and SWE algorithm

2019 Fall.Includes bibliographical references.Snow is an important wintertime property because it is a source of freshwater, regulates land-atmosphere exchanges, and increases the surface albedo of snow-covered regions. Unfortunately, in-situ observations of both snowfall and snow water equivalent (SWE) are globally sparse and point measurements are not representative of the surrounding area, especially in mountainous regions. The total amount of land covered by snow, which is climatologically important, is fairly straightforward to measure using satellite remote sensing. The total SWE is hydrologically more useful, but significantly more difficult to measure. Accurately measuring snowfall and SWE is an important first step toward a better understanding of the impacts snow has for hydrological and climatological purposes. Satellite passive microwave retrievals of snow offer potential due to consistent overpasses and the capability to make measurements during the day, night, and cloudy conditions. However, passive microwave snow retrievals are less mature than precipitation retrievals and have been an ongoing area of research. Exacerbating the problem, communities that remotely sense snowfall and SWE from passive microwave sensors have historically operated independently while the accuracy of the products has suffered because of the physical and radiometric dependency between the two. In this study, we assessed the relationship between the Northern Hemisphere snowfall and SWE products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). This assessment provides insight into regimes that can be used as a starting point for future improvements using coupled snowfall and SWE algorithm. SnowModel, a physically-based snow evolution modeling system driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, was employed to consistently compare snowfall and SWE by accounting for snow evolution. SnowModel has the ability to assimilate observed SWE values to scale the amount of snow that must have fallen to match the observed SWE. Assimilation was performed using AMSR-E, Canadian Meteorological Centre (CMC) Snow Analysis, and Snow Data Assimilation System (SNODAS) SWE to infer the required snowfall for each dataset. Observed AMSR-E snowfall and SWE were then compared to the MERRA-2 snowfall and SnowModel-produced SWE as well as SNODAS and CMC inferred snowfall and observed SWE. Results from the study showed significantly different snowfall and SWE bias patterns observed by AMSR-E. Specifically, snowfall was underestimated nearly globally and SWE had pronounced regions of over and underestimation. Snowfall and SWE biases were found to differ as a function of surface temperature, snow class, and elevation

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

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

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2&thinsp;cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5&thinsp;cm&thinsp;s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20&thinsp;cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03&thinsp;m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07&thinsp;m below the snow surface.</p