Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks

Changes in Arctic sea ice affect atmospheric circulation, ocean current, and polar ecosystems. There have been unprecedented decreases in the amount of Arctic sea ice due to global warming. In this study, a novel 1-month sea ice concentration (SIC) prediction model is proposed, with eight predictors using a deep-learning approach, convolutional neural networks (CNNs). This monthly SIC prediction model based on CNNs is shown to perform better predictions (mean absolute error - MAE - of 2.28 %, anomaly correlation coefficient - ACC - of 0.98, root-mean-square error - RMSE - of 5.76 %, normalized RMSE - nRMSE - of 16.15 %, and NSE - Nash-Sutcliffe efficiency - of 0.97) than a random-forest-based (RF-based) model (MAE of 2.45 %, ACC of 0.98, RMSE of 6.61 %, nRMSE of 18.64 %, and NSE of 0.96) and the persistence model based on the monthly trend (MAE of 4.31 %, ACC of 0.95, RMSE of 10.54 %, nRMSE of 29.17 %, and NSE of 0.89) through hindcast validations. The spatio-temporal analysis also confirmed the superiority of the CNN model. The CNN model showed good SIC prediction results in extreme cases that recorded unforeseen sea ice plummets in 2007 and 2012 with RMSEs of less than 5.0 %. This study also examined the importance of the input variables through a sensitivity analysis. In both the CNN and RF models, the variables of past SICs were identified as the most sensitive factor in predicting SICs. For both models, the SIC-related variables generally contributed more to predict SICs over ice-covered areas, while other meteorological and oceanographic variables were more sensitive to the prediction of SICs in marginal ice zones. The proposed 1-month SIC prediction model provides valuable information which can be used in various applications, such as Arctic shipping-route planning, management of the fishing industry, and long-term sea ice forecasting and dynamics

관측과 모델 비교를 통한 북극 혼합 구름의 미세물리 특성 및 복사효과 연구

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2023. 8. 김상우.Clouds have a major impact on the Earth's radiative budget and climate change, yet little microphysical data has been collected on clouds in the polar regions. This lack of microphysics data is related to the challenges of deploying and operating instruments in some of the world's most challenging and remote atmospheric environments. This thesis investigates the macro- and microphysical properties of clouds based on observations over Ny-Ålesund, Svalbard, in order to better understand the role of clouds in the Arctic. The total cloud occurrence was found to be ~77.6% from February 2017 to February 2023. The most predominant cloud type is multilayer clouds with a frequency of occurrence of 39.1%, and single-layer clouds with ~37.2%. The total occurrences of single-layer ice, liquid, and mixed-phase clouds are 19%, 4.4%, and 14.9%, respectively. In addition, surface measurements of upward and downward shortwave and longwave radiation from the Baseline Surface Radiation Network (BSRN) at Ny-Ålesund station were examined. Relatively lower values of upward and downward longwave fluxes for ice and mixed-phase clouds were highly correlated with cloud top temperature by phase. The database of cloud properties and the classification method obtained in this work are used to evaluate weather prediction models. We evaluated the microphysical properties of Arctic low-level clouds simulated by four cloud microphysics parameterization schemes (Morrison, WDM6, NSSL, and P3) implemented in the Polar-optimized Weather Research and Forecasting (PWRF) model. The evaluation is based on a comparison with data from the Arctic Cloud Observations Using Airborne Measurements during the Polar Day (ACLOUD) experiment, which took place near Svalbard in May-June 2017. A significant number of clouds were observed during the campaign, mainly due to adiabatic motions and sensible/latent heat fluxes that caused air masses to warm (by 4°C) as they were transported over the sea ice and ocean transition zone. The Morrison and WDM6 schemes performed best overall, with frequency bias (FB) values close to 1 (1.07, 1.13) and high log-odds ratios (0.50, 0.48) in predicting cloud occurrence, indicating good agreement with observed cloud occurrence. On the other hand, the NSSL and P3 schemes showed a high FB value (1.30, 1.56) with a low log-odds ratio (0.17, 0.16), indicating a high overestimation of cloud occurrence. Conversely, the WDM6 scheme produced higher ice-mixing ratios than the Morrison and NSSL schemes, while the latter two tended to produce more snow and graupel. However, all schemes generally underestimated both liquid and ice water content. Longwave downward (LWD) flux depends on atmospheric temperature and humidity, which are simulated differently by each cloud microphysics scheme. The model underestimated LWD flux is highly correlated with the LWC bias of each scheme. This study highlights the critical need for observational development of cloud parameterization in the Arctic to better estimate the impact of clouds on the Arctic climate under conditions of rapid Arctic warming.북극 증폭에 대한 구름의 영향은 구름의 특성뿐만 아니라 구름이 북극 기후의 구성 요소(수증기, 해양, 해빙, 알베도, 표면 온도 등)와 밀접하게 관련되어 있기 때문에 불확실하다. 이 논문에서는 북극에서 구름의 역할을 더 잘 이해하기 위해 Cloudnet의 스발바드 니알슨 지상 관측자료를 바탕으로 구름의 거시적 및 미시적 특성을 분석하였다. 2017년 2월부터 2023년 2월까지 구름의 총 발생률은 약 77.6%로 나타났다. 가장 우세한 구름 유형은 다층 구름으로 발생 빈도는 39.1%, 단층 구름은 ~37.2%이다. 단층 얼음, 액체, 혼합상 구름의 총 발생 빈도는 각각 19%, 4.4%, 14.9%이다. 또한, 니알슨 관측소의 Baseline Surface Radiation Network (BSRN) 복사 자료를 활용하여 상하 단파 및 장파 복사에 대한 측정값을 분석하였다. 얼음과 혼합상 구름에 대한 장파 상향 및 장파 하향 플럭스의 상대적으로 낮은 값은 위상별 구름 최고 온도와 높은 상관관계를 보였다. 본 연구를 통해 얻은 구름 특성 데이터베이스와 분류 방법은 기상 예측 모델 평가에 사용되었다. 극지에 최적화된 Polar-optimized Weather Research and Forecasting (PWRF) 모델에 구현된 4가지 구름 미세물리 매개변수화 방식(Morrison, WDM6, NSSL, P3)을 활용하여 시뮬레이션한 저층 북극 혼합 구름의 미시물리적 특성을 평가하였다. 이 분석은 2017년 5월부터 6월까지 스발바드 인근에서 진행된 Arctic Cloud Observations Using Airborne Measurements during the Polar Day (ACLOUD) 캠페인 데이터와의 비교를 기반으로 한다. 이 캠페인 기간 동안 상당수의 혼합 구름이 관측되었는데, 이는 주로 해빙과 해양 전이대를 통과하는 동안 기단이 가열(4°C)되는 단열 운동과 현열/잠열 플럭스로 인해 발생했다. Morrison 및 WDM6 구름 모수화 방식은 구름 발생 예측에서 높은 로그 확률(0.50, 0.48)과 함께 1에 가까운 빈도 편향 값(1.07, 1.13)을 나타내며 구름이 발생한 지역 및 높이와 잘 일치하는 것으로 나타나 전반적으로 가장 우수한 성능을 나타났다. NSSL 와 P3 모수화 스킴은 낮은 로그 확률 비율(0.17, 0.16)과 함께 높은 빈도 편향 값(1.30, 1.56)을 보여 구름 발생을 과대평가하는 것으로 나타났다. 반대로 WDM6 모수화 스킴은 Morrison 및 NSSL 방식에 비해 더 높은 얼음 혼합 비율을 생성한 반면, 후자의 두 방식은 더 많은 눈과 싸락눈을 생성하는 경향이 있었다. 모든 방식은 일반적으로 액체와 얼음 수분의 함량을 모두 과소평가했다. 뿐만 아니라, 혼합 구름의 복사율 분석을 통해 구름 타입에 따른 장파와 단파의 상대적 중요도를 평가하고, 구름의 미세물리적 특성에 대한 의존성을 분석하였다. 장파 하강 복사율은 대기 온도와 습도에 따라 달라지며, 모델에서 과소평가된 장파 하강 복사율은 각 구름 모수화 스킴에서 모의된 구름 수분 함량 편향과 높은 상관관계를 보였다. 이 연구는 급격한 북극 온난화 조건에서 구름이 북극 기후에 미치는 영향을 보다 정확하게 추정하기 위해 북극 구름의 타입구분이 중요하며, 북극의 구름 매개 변수화에 대한 관측 기반 개발이 시급히 필요하다.Chapter 1. Introduction １ 1.1 Background and motivation １ 1.2 Scientific Questions ５ 1.3 Objectives of this study ８ Chapter 2. Data and model description ９ 2.1 ACLOUD campaign ９ 2.2 Cloud radar data １１ 2.3 Surface radiation data １３ 2.4 PWRF model configuration １４ Chapter 3. Arctic cloud properties at Ny-Ålesund, Svalbard １９ 3.1 Definition of diagnostics １９ 3.2 Classification of hydrometeors and clouds properties ２４ 3.3 Statistics of Arctic mixed-phase clouds and their radiative effect ３７ Chapter 4. Arctic mixed-phase clouds: comparison between observation and model 4 5 4.1 Meteorological contexts during the ACLOUD campaign ４９ 4.2 Cloud microphysical properties: Observation vs. model ５４ 4.3 Theoretical analysis of scheme algorithm ７７ 4.4 Radiative forcing at the surface ８７ Chapter 5. Summary and future direction ９１ References ９６ 국문 초록 １２１박

Research and technology, 1990: Goddard Space Flight Center

Goddard celebrates 1990 as a banner year in space based astronomy. From above the Earth's obscuring atmosphere, four major orbiting observatories examined the heavens at wavelengths that spanned the electromagnetic spectrum. In the infrared and microwave, the Cosmic Background Explorer (COBE), measured the spectrum and angular distribution of the cosmic background radiation to extraordinary precision. In the optical and UV, the Hubble Space Telescope has returned spectacular high resolution images and spectra of a wealth of astronomical objects. The Goddard High Resolution Spectrograph has resolved dozens of UV spectral lines which are as yet unidentified because they have never before been seen in any astronomical spectrum. In x rays, the Roentgen Satellite has begun returning equally spectacular images of high energy objects within our own and other galaxies

Investigation of Sea Ice Using Multiple Synthetic Aperture Radar Acquisitions

The papers of this thesis are not available in Munin. Paper I: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Tomographic imaging of fjord ice using a very high resolution ground-based SAR system. Available in IEEE Transactions on Geoscience and Remote Sensing, 55 (2):698-714. Paper II: Yitayew, T. G., Ferro-Famil, L., Eltoft, T. & Tebaldini, S. (2017). Lake and fjord ice imaging using a multifrequency ground-based tomographic SAR system. Available in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(10):4457-4468. Paper III: Yitayew, T. G., Divine, D. V., Dierking, W., Eltoft, T., Ferro-Famil, L., Rosel, A. & Negrel, J. Validation of Sea ice Topographic Heights Derived from TanDEMX Interferometric SAR Data with Results from Laser Profiler and Photogrammetry. (Manuscript).The thesis investigates imaging in the vertical direction of different types of ice in the arctic using synthetic aperture radar (SAR) tomography and SAR interferometry. In the first part, the magnitude and the positions of the dominant scattering contributions within snow covered fjord and lake ice layers are effectively identified by using a very high resolution ground-based tomographic SAR system. Datasets collected at multiple frequencies and polarizations over two test sites in Tromsø area, northern Norway, are used for characterizing the three-dimensional response of snow and ice. The presented experimental results helped to improve our understanding of the interaction between radar waves and snow and ice layers. The reconstructed radar responses are also used for estimating the refractive indices and the vertical positions of the different sub-layers of snow and ice. The second part of the thesis deals with the retrieval of the surface topography of multi-year sea ice using SAR interferometry. Satellite acquisitions from TanDEM-X over the Svalbard area are used for analysis. The retrieved surface height is validated by using overlapping helicopter-based stereo camera and laser profiler measurements, and a very good agreement has been found. The work contributes to an improved understanding regarding the potential of SAR tomography for imaging the vertical scattering distribution of snow and ice layers, and for studying the influence of both sensor parameters such as its frequency and polarization and scene properties such as layer stratification, air bubbles and small-scale roughness of the interfaces on snow and ice backscattered signal. Moreover, the presented results reveal the potential of SAR interferometry for retrieving the surface topography of sea ice

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

The new BELUGA setup for collocated turbulence and radiation measurements using a tethered balloon: First applications in the cloudy Arctic boundary layer

The new BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) tethered balloon system is introduced. It combines a set of instruments to measure turbulent and radiative parameters and energy fluxes. BELUGA enables collocated measurements either at a constant altitude or as vertical profiles up to 1.5km in height. In particular, the instrument payload of BELUGA comprises three modular instrument packages for high-resolution meteorological, wind vector and broadband radiation measurements. Collocated data acquisition allows for estimates of the driving parameters in the energy balance at various heights. Heating rates and net irradiances can be related to turbulent fluxes and local turbulence parameters such as dissipation rates. In this paper the technical setup, the instrument performance, and the measurement strategy of BELUGA are explained. Furthermore, the high vertical resolution due to the slow ascent speed is highlighted as a major advantage of tethered balloon-borne observations. Three illustrative case studies of the first application of BELUGA in the Arctic atmospheric boundary layer are presented. As a first example, measurements of a single-layer stratocumulus are discussed. They show a pronounced cloud top radiative cooling of up to 6K h-1. To put this into context, a second case elaborates respective measurements with BELUGA in a cloudless situation. In a third example, a multilayer stratocumulus was probed, revealing reduced turbulence and negligible cloud top radiative cooling for the lower cloud layer. In all three cases the net radiative fluxes are much higher than turbulent fluxes. Altogether, BELUGA has proven its robust performance in cloudy conditions of the Arctic atmospheric boundary layer

Multi-sensor Cloud and Aerosol Retrieval Simulator and Its Applications

Executing a cloud or aerosol physical properties retrieval algorithm from controlled synthetic data is an important step in retrieval algorithm development. Synthetic data can help answer questions about the sensitivity and performance of the algorithm or aid in determining how an existing retrieval algorithm may perform with a planned sensor. Synthetic data can also help in solving issues that may have surfaced in the retrieval results. Synthetic data become very important when other validation methods, such as field campaigns,are of limited scope. These tend to be of relatively short duration and often are costly. Ground stations have limited spatial coverage whilesynthetic data can cover large spatial and temporal scales and a wide variety of conditions at a low cost. In this work I develop an advanced cloud and aerosol retrieval simulator for the MODIS instrument, also known as Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In a close collaboration with the modeling community I have seamlessly combined the GEOS-5 global climate model with the DISORT radiative transfer code, widely used by the remote sensing community, with the observations from the MODIS instrument to create the simulator. With the MCARS simulator it was then possible to solve the long standing issue with the MODIS aerosol optical depth retrievals that had a low bias for smoke aerosols. MODIS aerosol retrieval did not account for effects of humidity on smoke aerosols. The MCARS simulator also revealed an issue that has not been recognized previously, namely,the value of fine mode fraction could create a linear dependence between retrieved aerosol optical depth and land surface reflectance. MCARS provided the ability to examine aerosol retrievals against “ground truth” for hundreds of thousands of simultaneous samples for an area covered by only three AERONET ground stations. Findings from MCARS are already being used to improve the performance of operational MODIS aerosol properties retrieval algorithms. The modeling community will use the MCARS data to create new parameterizations for aerosol properties as a function of properties of the atmospheric column and gain the ability to correct any assimilated retrieval data that may display similar dependencies in comparisons with ground measurements

Earth Observatory Satellite (EOS) Definition Phase Report, Volume 1

System definition studies were conducted of the Earth Observatory Satellite (EOS). The studies show that the concept of an Earth Observatory Satellite in a near-earth, sun-synchronous orbit would make a unique contribution to the goals of a coordinated program for acquisition of data for environmental research with applications to earth resource inventory and management. The technical details for the proposed development of sensors, spacecraft, and a ground data processing system are presented

Aerosol-Cloud-Precipitation Interactions - Studied using combinations of remote sensing and in-situ data

Cloud droplets never form in the atmosphere without a seed in the form of an aerosol particle. Changes in number concentrations of aerosol particles in the atmosphere can therefore affect the number of droplets in a cloud. Higher concentrations of aerosol particles in the atmosphere lead to clouds with more droplets and if the amount of liquid water in the clouds stay the same, the droplets become smaller. Clouds with more, smaller droplets reflect more sunlight and may take longer to produce precipitation. In the research presented in this thesis, satellite data of clouds are combined with a range of other datasets to investigate how sensitive the cloud properties are to changes in the concentration of aerosol particles. Cloud droplets were found to be smaller in low-level clouds formed in air with higher aerosol number concentrations over the ocean north of Scandinavia. This was also true for low-level and convective clouds over land in Sweden and Finland. The results regarding cloud optical thickness (COT), which is a measure of how much light a cloud reflects, was not as conclusive. For the low-level clouds over the ocean, the COT was higher in air masses with higher aerosol number concentrations. Differences in meteorological conditions in the clean and polluted air masses may however explain some of the differences in COT. The low-level and convective clouds over land did not show any significant changes in COT with varying aerosol number concentrations. This may be caused by changes in cloud dynamics due to the smaller droplets in the clouds. Hence, the indirect aerosol effect could not be observed for clouds studied over land. The precipitation intensity from the clouds over land and how this varied with changing aerosol loading was also investigated. For both low-level and convective clouds, the precipitation was found to decrease somewhat with increasing aerosol number concentrations. However, for the convective clouds, this relationship only appeared when the clouds were sorted according to vertical extent, as higher convective clouds tend to produce heavier precipitation. How cirrus clouds at midlatitudes in the northern hemisphere are affected by the mass concentration of particulate sulphate present in the lowermost stratosphere (LMS) was investigated using satellite data. Changes in the LMS particle levels were caused by explosive volcanos that emit gases and particles into the stratosphere. Due to subsidence in the stratosphere at midlatitudes, the volcanic sulphate eventually enters the upper troposphere, increasing its sulphate concentration. The reflectance of the cirrus clouds decreased when there were more sulphate particles present in the LMS. Cirrus clouds warm the climate and a decrease in their reflectance hence cools the climate