Global Change Observation Mission Research Articles

RELATIONSHIP BETWEEN CHLOROPHYLL-A AND SEA SURFACE TEMPERATUR WITH Sardinella lemuru CATCHING IN BALI STRAIT

The Bali Strait is one of Indonesia's territorial waters that have high natural resource potential. The area is only about 2,500 km2 but has a high potential fishing ground. The Bali Strait has unique and dynamic waters that can cause fluctuations in fish production amount each year. The largest type of fish caught in the Bali Strait is lemuru (Sardinella lemuru), a fish found only in the Bali Strait. This fish plays a significant role in the economy of fishers in the Bali Strait. Each year the catch of lemuru has fluctuated, making fishing locations challenging to predict. Sea Surface Temperature (SST) and Chlorophyll-a (Chl-a) are oceanographic parameters that can affect the resources of the ocean. Oceanographic phenomena, such as upwelling, can also influence the condition of fish resources. Therefore, understanding the relationships between these factors is essential in practical fisheries management. Observation of oceanographic factors is very hard with the field observation due to time and cost limitations. The remote sensing technique is an efficient method to determine SST distributions and Chl-a concentrations using satellite imagery. This study analyzes SST and Chl-a concentration in the Bali Strait using the Global Change Observation Mission(GCOM-C) satellite and determines the correlation between Chl-a and SST with a total fish catch(lemuru) during 2019. The results showed the maximum average Chl-a concentration observed on August 1.62 mg/m3 and the lowest concentration observed on January 0.45 mg/m3, the maximum SST on March was 28.12° C, and on August (Dry season) with 22.40° C. The SST variable's influence provides a negative correlation (R = -0.209) with changes in lemuru catch, while the Chl-a parameter has a positive correlation (R = 0.375) with changes in the catch. The pattern of relationship between Chl-a with fish catching had a good relationship than the SST in 2019.Keywords: Bali Strait; Sardinella lemuru; SST; Chl-a.

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States

This study first compares two different passive microwave snow water equivalent (SWE) retrievals, namely the retrieval from the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) and that from the Global Change Observation Mission – Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2); it further creates an optimal blending mechanism that merges the two retrievals with in situ observations from the Snow Telemetry (SNOTEL) and Cooperative Observer Program (COOP) networks. The assessments of the two products are done over conterminous United States (CONUS) for the snow seasons (November–June) of the water years 2017–2019 using in situ data and the SNOw Data Assimilation System (SNODAS) SWE analysis. Both satellite products tend to underestimate SWE. Between the two, AMSR2 retrieval outperforms in terms of correlation with observations and depth of saturation, but it exhibits a distinctive, seasonally varying bias that is not seen in ATMS retrieval. The negative bias over the early snow season, as further analysis indicates, most likely stems from AMSR2 retrieval's use of a high frequency channel (i.e., 89 GHz) for shallow snow detection, while the impact of differing assumptions of snow density is marginal. The blending scheme, developed on the basis of the validation experiment, features a histogram-based bias correction as a supplement to optimal interpolation. Cross-validation suggests that interpolated station product without the satellite background broadly underperforms the blended in situ-satellite product, confirming the utility of the satellite retrievals. Furthermore, the a priori bias correction mechanism is shown to be effective in mitigating large fluctuations in bias. Finally, the bias-corrected, blended in situ-satellite product performs comparably or even favorably against SNODAS over many parts of the CONUS, with important implications for joint use of satellite and in situ observations for hydrological monitoring and forecasting.

A Fundamental Climate Data Record Derived from AMSR-E, MWRI, and AMSR2

Fundamental climate data records (FCDRs) play a vital role in monitoring climate change. In this article, we develop a spaceborne passive microwave-based FCDR by recalibrating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) on the Aqua satellite, the microwave radiometer imager (MWRI) onboard the Feng-Yun-3B (FY3B) satellite, and the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the JAXA’s Global Change Observation Mission first-Water (GCOM-W1) satellite. Before recalibration, it is found that AMSR-E and AMSR2 observations are stable over time, but MWRI drifted colder before May 2015 and had nonnegligible errors in geolocation for most channels. In addition, intersensor differences of brightness temperatures (TBs) are as large as 5–10 K. To improve data consistency and continuity, several intersensor calibration methods are applied by using AMSR2 as a reference while using MWRI to bridge the data gap between AMSR2 and AMSR-E. The double difference method is used to provide intersensor difference time series for correcting calibration biases, such as scene temperature-dependent bias, solar-heating-induced bias, and systematic constant bias. Hardware differences between sensors are corrected using principal component analysis. After recalibration, the mean biases of both MWRI and AMSR-E are less than 0.3 K compared to the AMSR2 reference and their standard deviations are less than 1 K for all channels. Under oceanic rain-free conditions, the TB biases are less than 0.2 K for all channels and no significant relative bias drifts were found between sensors for overlapping observations. These statistics suggest that the consistency between these instruments was significantly improved and the derived FCDR could be useful to obtain long-term water cycle-related variables for climate research.

Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

Temporal Variation and Spatial Structure of the Kuroshio-Induced Submesoscale Island Vortices Observed from GCOM-C and Himawari-8 Data

Dynamics of ocean current-induced island wake has been an important issue in global oceanography. Green Island, a small island located off southeast of Taiwan on the Kuroshio path was selected as the study area to more understand the spatial structure and temporal variation of well-organized vortices formed by the interaction between the Kuroshio and the island. Sea surface temperature (SST) and chlorophyll-a (Chl-a) concentration data derived from the Himawari-8 satellite and the second generation global imager (SGLI) of global change observation mission (GCOM-C) were used in this study. The spatial SST and Chl-a variations in designed observation lines and the cooling zone transitions on the left and right sides of the vortices were investigated using 250 m spatial resolution GCOM-C data. The Massachusetts Institute of Technology general circulation model (MITgcm) simulation confirmed that the positive and negative vortices were sequentially detached from each other in a few hours. In addition, totals of 101 vortexes from July 2015 to December 2019 were calculated from the 1-h temporal resolution Himawari-8 imagery. The average vortex propagation speed was 0.95 m/s. Totals of 38 cases of two continuous vortices suggested that the average vortex shedding period is 14.8 h with 1.15 m/s of the average incoming surface current speed of Green Island, and the results agreed to the ideal Strouhal-Reynolds number fitting curve relation. Combined with the satellite observation and numerical model simulation, this study demonstrates the structure of the wake area could change quickly, and the water may mix in different vorticity states for each observation station.

Radiometric Model and Inter-Comparison Results of the SGLI-VNR On-Board Calibration

The Second Generation Global Imager (SGLI) on Global Change Observation Mission–Climate (GCOM-C) satellite empowers surface and atmospheric measurements related to the carbon cycle and radiation budget, with two radiometers of Visible and Near Infrared Radiometer (SGLI-VNR) and Infrared Scanning Radiometer (SGLI-IRS) that perform a wide-band (380 nm–12 µm) optical observation not only with as wide as a 1150–1400 km field of view (FOV), but also with as high as 0.25–0.5 km resolution. Additionally, polarization and along-track slant view observations are quite characteristic of SGLI. It is important to calibrate radiometers to provide the sensor data records for more than 28 standard products and 23 research products including clouds, aerosols, ocean color, vegetation, snow and ice, and other applications. In this paper, the radiometric model and the first results of on-board calibrations on the SGLI-VNR, which include weekly solar and light-emitting diode (LED) calibration and monthly lunar calibration, will be described. Each calibration data was obtained with corrections, where beta angle correction and avoidance of reflection from multilayer insulation (MLI) were applied for solar calibration; LED temperature correction was performed for LED calibration; and the GIRO (GSICS (Global Space-based Inter-Calibration System) Implementation of the ROLO (RObotic Lunar Observatory) model) model was used for lunar calibration. Results show that the inter-comparison of the relative degradation amount between these three calibrations agreed to within 1% or less.

L-Band Passive Microwave Data from SMOS for River Gauging Observations in Tropical Climates

The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

A comprehensive 4D-Var vortex initialization using a nonhydrostatic axisymmetric TC model with convection accounted for

A four-dimensional variational (4D-Var) vortex initialization (VI) system is developed for a nonhydrostatic axisymmetric numerical model with convection accounted for (the RE87 model). Derivations of the tangent linear and adjoint models of the RE87 model and the correctness checks are presented. As an initial evaluation of the 4D-Var VI system, a cost function that measures the model fit to satellite microwave retrievals of tropical cyclone (TC), warm-core temperatures and total precipitable water (TPW) from the following four polar-orbiting satellites within a slightly longer than 1-h assimilation window is minimized using the limited-memory quasi-Newton minimization algorithm: the Suomi National Polar-orbiting Partnership, NOAA-20, Fengyun-3D and Global Change Observation Mission—Water Satellite 1. An azimuthal spectral analysis in cylindrical coordinates centred on the TC centres shows that the warm core and TPW fields within TCs are dominated by the axisymmetric component. The 4D-Var VI results assimilating the axisymmetric component of the above satellite retrievals produced a significant reduction in the cost function and the norm of the gradient as the minimization process is completed. The gradient of the cost function is accurately computed by a single integration of the RE87 adjoint model. In the cases of Hurricane Florence and Typhoon Mangkhut, improved forecast of intensifications and more realistic vertical structures of all model state variables (e.g. temperature, water vapour mixing ratio, liquid water content mixing ratio, tangential and radial wind components and vertical velocity) are obtained when compared with a parallel run initialized simply by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis.

GCOM-C Data Validation Plan for Land, Atmosphere, Ocean, and Cryosphere

Japan Aerospace Exploration Agency (JAXA) will launch an Earth observing satellite for climate studies named “Global Change Observation Mission – Climate (GCOM-C)” in 2017 which carries a multi-spectral optical sensor named Second-Generation Global Imager (SGLI). The GCOM-C satellite will observe various geophysical variables such as vegetation, land surface temperature, aerosol, clouds, ocean color, sea surface temperature, snow cover extent, snow grain size and so on. The objectives of the SGLI observations are to elucidate the roles of the geophysical variables in the recent changing Earth's climate system and to establish long-term satellite data record of the variables. Three kinds of target accuracies to be achieved for the SGLI observations are defined for evaluating the success of the GCOM-C satellite mission. First one is the minimum thresholds to be achieved for the first SGLI data release at one year after the launch of GCOM-C. Second and third thresholds are the standard and goal accuracies to be achieved at five years after the launch for evaluating the full and extra success of the mission. The target accuracies for individual SGLI products are available at the GCOM-C web site (http://suzaku.eorc.jaxa.jp/GCOM_C/index.html). The quality and accuracies of SGLI products are planned to be evaluated and maintained through validation activities organized by the SGLI validation team consisting of JAXA and SGLI principal investigators (PIs). Uncertainties of the SGLI data products will be characterized through the comparison with in-situ observations, similar products derived from other satellites, climatological data, and/or numerical model simulations. This paper summarizes the overall validation plan for the SGLI geophysical variable products.

Application of GCOM-W AMSR2 and S-NPP ATMS Hydrological Products to a Flooding Event in the United States

Satellite remotely sensed products provide critical information to weather forecasters at the National Oceanic and Atmospheric Administration (NOAA) and greatly supplement spare or nonexistent surface observations. Low earth orbiting passive microwave sensors provide unique information related to atmospheric moisture and precipitation, as well as surface properties such as oceanic wind speed. This paper will focus on two microwave sensors that are part of NOAA's Joint Polar Satellite System—the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Technology Microwave Sounder (ATMS)—which are flown on Japan's Global Change Observation Mission—Water (GCOM-W1) and the Suomi National Polar orbiting Partnership (S-NPP) satellites. The orbits of GCOM-W1 and S-NPP are such that AMSR2 and ATMS observe the same region of the earth nearly the same time of the day. This allows for comparison of similar products from the two sensors, which is of great interest to users such as NOAA's National Weather Service. In this paper, we focus on product comparisons for a historic flooding event in the U.S. associated with Hurricane Joaquin. The microwave products are presented and shown with other widely used in-situ observations. The performance of AMSR2 and ATMS products are compared. The higher spatial resolution of the AMSR2 sensor provides more detailed information on water vapor and rain rate compared to ATMS. On the other hand, the wider swath width of the ATMS provides a more continuous field of water vapor and precipitation, as well as water vapor retrievals over land.

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature (PCT) and scattering index (SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h-1 and 7.29 mm h-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h-1 and 9.35 mm h-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers.

A MODIS-Derived Value-Added Climatology of Maritime Cloud Liquid Water Path That Conserves Solar Reflectance

AbstractA dataset is generated from a method to retrieve distributions of cloud liquid water path over partially cloudy scenes. The method was introduced in a 2011 paper by Foster and coauthors that described the theory and provided test cases. Here it has been applied to Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and collection-6 cloud products, resulting in a value-added dataset that contains adjusted distributions of cloud liquid water path for more than 10 years for marine liquid cloud for both Aqua and Terra. This method adjusts horizontal distributions of cloud optical properties to be more consistent with observed visible reflectance and is especially useful in areas where cloud optical retrievals fail or are considered to be of low quality. Potential uses of this dataset include validation of climate and radiative transfer models and facilitation of studies that intercompare satellite records. Results show that the fit method is able to reduce bias between observed visible reflectance and that derived from optical retrievals by up to an average improvement of 3%. The level of improvement is dependent on several factors, including seasonality, viewing geometry, cloud fraction, and cloud heterogeneity. Applications of this dataset are explored through a satellite intercomparison with PATMOS-x and Global Change Observation Mission–First Water (GCOM-W1; “SHIZUKU”) AMSR-2 and use of a Monte Carlo radiative transfer model. From the 3D Monte Carlo model simulations, albedo biases are found when the method is applied, with seasonal averages that range over 0.02–0.06.

A New High-Resolution Sea Surface Temperature Blended Analysis

AbstractThe National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite, Data, and Information Service (NESDIS) now generates a daily 0.05° (∼5 km) global high-resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines SST data from U.S., Japanese, and European geostationary infrared imagers, and low-Earth-orbiting infrared (United States and Europe) SST data, into a single high-resolution 5-km product. An earlier version produced a 0.1° (∼11 km) resolution, a resolution chosen to approximate the Nyquist sampling criterion for the midlatitude Rossby radius (∼20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher-resolution grid spacing has more success in this regard. The analysis employs a rigorous multiscale optimum interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The product accuracy verified against globally distributed buoys is ∼0.02 K, with a robust standard deviation of ∼0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction. Forthcoming enhancements include the incorporation of microwave SST products from low-Earth-orbiting platforms [e.g., Global Change Observation Mission for Water-1 (GCOM-W1)] in order to improve the resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper-ocean heating.

Does AMSR2 produce better soil moisture retrievals than AMSR-E over Australia?

The Advanced Microwave Scanning Radiometer 2 (AMSR2), a follow-up microwave sensor to the AMSR for Earth Observing System (AMSR-E), was launched on the Global Change Observation Mission 1 – Water (GCOM-W1) satellite in May 2012. It is as yet unclear if instrumental improvements in AMSR2 over AMSR-E have led to better soil moisture (SM) estimates, especially since there is no overlapping period of data between the sensors. This study focuses on comparing the results of AMSR2 and AMSR-E SM over Australia, distinguishing four Köppen climate zones to determine if AMSR2 is better than AMSR-E. This is achieved by selecting two year-long comparative time periods from the operating periods of AMSR-E and AMSR2, based on their statistical similarities in modeled SM as a proxy, using Modern Era Retrospective-analysis for Research and Applications-Land (MERRA-L). The AMSR2 and AMSR-E C- and X-band SM derived from the Land Parameter Retrieval Model (LPRM) was evaluated. Both AMSR2 C- and X-band SM products were found to show similar temporal patterns and spatial agreement with AMSR-E C- and X-band SM, supported by unbiased root mean square difference (ubRMSD) and R-values with MERRA-L SM, respectively. Using lag-based instrumental variable analysis to estimate the random error component of SM retrievals, the noise-to-signal ratios in AMSR2 X-band SM were found to be slightly higher than their AMSR-E counterparts. The improvements in AMSR2, such as the superior radiometric sensitivity and spatial resolution, have therefore not led to statistically significant differences in performance for LPRM retrievals at 1/2°×1/2° grid resolution, when compared with AMSR-E. However, similarities in the metrics for AMSR2 and AMSR-E SM suggest that AMSR2 provides a valuable continuation to AMSR-E.

Global Land Surface Emissivity Estimation From AMSR2 Observations

A reliable estimate of emissivity is critical for a wide range of applications for the atmosphere, the biosphere, the lithosphere, the cryosphere, and the hydrosphere. This study uses three years (August 2012 to July 2015) of data from the Advanced Microwave Scanning Radiometer-2 sensor that is onboard the Global Change Observation Mission 1st Water satellite to explore estimates of instantaneous global land emissivity. A method is adopted to remove the known inconsistency in penetration depths between microwave brightness temperatures and infrared-based ancillary data that could cause differences between day and night emissivity estimates. After removing the diurnal atmospheric effects, the resulting retrieved cloud-free land emissivities realistically represent well-known large-scale features. As expected, the polarization differences of estimated emissivities show noticeable seasonal variations over the deciduous woodland and grassland regions due to changes in vegetation density. The potential of estimated emissivities for high-latitude snow detection and freeze/thaw state identification is also demonstrated.

Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2

AbstractExperiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows that the VIIRS and AMSR2 datasets yield analyses with similar global average errors, with the VIIRS analysis performing better during some seasons and the AMSR2 analysis performing better in others. Seasonal cloudiness in some regions diminishes the availability of VIIRS retrievals, resulting in better performance by the AMSR2 analysis. Both datasets were assimilated together along with data from the Advanced Very High Resolution Radiometer (AVHRR), ice data, and in situ data in an updated version of the CMC analysis produced on a 0.1° grid. Verification against independent data shows that the new analysis performed very well, with global average standard deviation consistently better than that of the international Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) real-time system. This analysis is shown to outperform the currently operational CMC SST analysis, with most of the improvement being due to its assimilation of the VIIRS and AMSR2 retrievals and a further small gain being due to changes to the analysis methodology (including higher resolution).

GCOM-W1 AMSR2 Level 1R Product: Dataset of Brightness Temperature Modified Using the Antenna Pattern Matching Technique

The Global Change Observation Mission 1st-Water (GCOM-W1) satellite, which was launched by the Japan Aerospace Exploration Agency in May 2012, contains an Advanced Microwave Scanning Radiometer-2 (AMSR2). The AMSR2 provides multifrequency measurements of microwave energy (brightness temperature) emitted by the Earth's surface and atmosphere. However, each frequency field of view (FOV) differs in size because of its hardware design. For the retrieval of more accurate geophysical parameters from multifrequency brightness temperatures, the brightness temperatures should be modified to be the same as measured in the same FOV. The Backus–Gilbert (BG) method is one of the antenna pattern matching techniques used for this modification. We applied the BG method to the AMSR2 data to define a new data set of modified brightness temperatures, a level 1R (L1R) product that is freely and widely available. We optimized the implementation of the BG method to obtain the L1R product, with smoothing factors dynamically determined for all modified brightness temperatures. This paper describes the implementation method, including the criterion used to determine the smoothing factors and presents a quality assessment of the L1R product.

Geophysical Model Function for the AMSR2 C-Band Wind Excess Emissivity at High Winds

Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission–Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012–2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature $(T_{B})$ at the surface under TCs, combining theoretical modeling and measured $T_{B}$ analyses. Estimated oceanic $T_{B}$ 's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.

An In-Depth Evaluation of Heritage Algorithms for Snow Cover and Snow Depth Using AMSR-E and AMSR2 Measurements

AbstractThe Advanced Microwave Scanning Radiometer 2 (AMSR2) was launched in 2012 on board the Global Change Observation Mission 1st–Water (GCOM-W1) satellite. This study presents a robust evaluation of AMSR2 algorithms for the retrieval of snow-covered area (SCA) and snow depth (SD) that will be used operationally by the National Oceanic and Atmospheric Administration (NOAA). Quantitative assessment of the algorithms was performed for a 10-yr period with AMSR-E and a 2-yr period with AMSR2 data using the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and in situ SD data as references. AMSR-E SCA showed a monthly overall accuracy rate of about 80% except in May. Accuracy improves significantly to over 90% when wet snow cases are excluded, and accuracy differences between ascending and descending portions of orbits also decrease. Microwave-derived SCA over dry snow areas can therefore be obtained with accuracy close to optically derived SCA. An evaluation of the results for AMSR-E SD showed a low overall bias of 1 cm and a root-mean-square error of 20 cm. Results for AMSR2-based SCA and SD are similar to those from AMSR-E. Biases and root-mean-square errors show dependencies on elevation, forest fraction, the magnitude of snow depth, and snow cover class.