A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km ? 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59

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

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models

Sea Ice Thickness, Freeboard, and Snow Depth products from Operation IceBridge Airborne Data

The study of sea ice using airborne remote sensing platforms provides unique capabilities to measure a wide variety of sea ice properties. These measurements are useful for a variety of topics including model evaluation and improvement, assessment of satellite retrievals, and incorporation into climate data records for analysis of interannual variability and long-term trends in sea ice properties. In this paper we describe methods for the retrieval of sea ice thickness, freeboard, and snow depth using data from a multisensor suite of instruments on NASA's Operation IceBridge airborne campaign. We assess the consistency of the results through comparison with independent data sets that demonstrate that the IceBridge products are capable of providing a reliable record of snow depth and sea ice thickness. We explore the impact of inter-campaign instrument changes and associated algorithm adaptations as well as the applicability of the adapted algorithms to the ongoing IceBridge mission. The uncertainties associated with the retrieval methods are determined and placed in the context of their impact on the retrieved sea ice thickness. Lastly, we present results for the 2009 and 2010 IceBridge campaigns, which are currently available in product form via the National Snow and Ice Data Cente

Snow observations from Arctic Ocean Soviet drifting stations: legacy and new directions

The Arctic Ocean is one of the most rapidly changing regions on the planet. Its warming climate has driven reductions in the region's sea ice cover which are likely unprecedented in recent history, with many of the environmental impacts being mediated by the overlying snow cover. As well as impacting energetic and material fluxes, the snow cover also obscures the underlying ice from direct satellite observation. While the radar waves emitted from satellite-mounted altimeters have some ability to penetrate snow cover, an understanding of snow geophysical properties remains critical to remote sensing of sea ice thickness. The paucity of Arctic Ocean snow observations was recently identified as a key knowledge gap and uncertainty by the Intergovernmental Panel on Climate Change's Special Report on Oceans and Cryosphere in a Changing Climate. This thesis aims to address that knowledge gap. Between 1937 and 1991 the Soviet Union operated a series of 31 crewed stations which drifted around the Arctic Ocean. During their operation, scientists took detailed observations of the atmospheric conditions, the physical oceanography, and the snow cover on the sea ice. This thesis contains four projects that feature these observations. The first two consider a well known snow depth and density climatology that was compiled from observations at the stations between 1954 & 1991. Specifically, Chapter two considers the role of seasonally evolving snow density in sea ice thickness retrievals, and Chapter three considers the impact of the climatological treatment itself on satellite estimates of sea ice thickness variability and trends. Chapter four presents a statistical model for the sub-kilometre distribution of snow depth on Arctic sea ice through analysis of snow depth transect data. Chapter five then compares the characteristics of snow melt onset at the stations with satellite observations and results from a recently developed model

The Global Observing System in the Assimilation Context

Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues

Improving satellite-based monitoring of the Arctic polar regions: identification of research and capacity gaps

We present a comprehensive review of the current status of remotely sensed and in situ sea ice, ocean, and land parameters acquired over the Arctic and Antarctic and identify current data gaps through comparison with the portfolio of products provided by Copernicus services. While we include several land parameters, the focus of our review is on the marine sector. The analysis is facilitated by the outputs of the KEPLER H2020 project. This project developed a road map for Copernicus to deliver an improved European capacity for monitoring and forecasting of the Polar Regions, including recommendations and lessons learnt, and the role citizen science can play in supporting Copernicus’ capabilities and giving users ownership in the system. In addition to summarising this information we also provide an assessment of future satellite missions (in particular the Copernicus Sentinel Expansion Missions), in terms of the potential enhancements they can provide for environmental monitoring and integration/assimilation into modelling/forecast products. We identify possible synergies between parameters obtained from different satellite missions to increase the information content and the robustness of specific data products considering the end-users requirements, in particular maritime safety. We analyse the potential of new variables and new techniques relevant for assimilation into simulations and forecasts of environmental conditions and changes in the Polar Regions at various spatial and temporal scales. This work concludes with several specific recommendations to the EU for improving the satellite-based monitoring of the Polar Regions