Surface Temperature Estimates Research Articles

A calibration-free, robust estimation of monthly land surface evapotranspiration rates for continental-scale hydrology

Abstract Continuous simulation of monthly evapotranspiration rates for 1979–2015 was performed by the latest, calibration-free version of the complementary relationship of evaporation over the conterminous United States. The results were compared to similar estimates of the WREVAP program and the North American Regional Reanalysis (NARR) project. Validation of the three methods was performed by the Parameter-Elevation Regressions on Independent Slopes Model precipitation and Hydrologic Unit Code level-6 runoff data. The present method outperforms the WREVAP and NARR estimates with a root-mean-square error (RMSE) of 89 mm yr−1, an R2 value of 0.87, an absolute bias (σ) of −5 mm yr−1, and slope (m) and intercept (c) values of 0.97 and 22 mm yr−1, respectively, for the best-fit line, in comparison to similar values (RMSE = 161 mm yr−1, R2 = 0.8, σ = 124 yr–1 mm yr−1, m = 0.88, c = 191 mm yr−1; and RMSE = 195 mm yr−1, R2 = 0.81, σ = 146 mm yr−1, m = 1.05, c = 120 mm yr−1) of the latter two methods. The value of the Priestley–Taylor (PT) coefficient was determined by inversion of the PT-equation via a model-independent identification of wet cells and their estimated surface temperatures.

An algorithm to retrieve Land Surface Temperature using Landsat-8 Dataset

Soil moisture, surface temperature, and vegetation are variables that play an important role in our environment which in turn increases the demand for accurate estimation of certain geophysical parameters such as weather, flooding, and land classification. However, for accurate Land Surface Temperature (LST) estimation, remotely sensed data of key environmental forms were considered and applied in this research. The goal of this study was to apply a suitable algorithm for LST estimation from the Landsat-8 dataset that gives a great accuracy when compared with in-situ observations. Spatial and temporal Landsat-8 data were acquired which provided the analytical structure for linking specific data successfully due to fine resolutions. The data were then applied to determine brightness temperatures, vegetation cover, and surface emissivity which demonstrated the effectiveness of the Split-Window Algorithm as an optimum method for LST retrieval from satellite. The results show temperature variation over a long period of time can be used in observing varying temperature values based on terrain i.e. High temperatures in fully built up areas and low temperatures in the well-vegetated regions. Finally, accurate LST estimation is important for land classification, energy budget estimations as well as agricultural production. Keywords: Emissivity, Landsat, Land Surface Temperature, Split-Window, Vegetation

Reconciling the opposing effects of warming on phytoplankton biomass in 188 large lakes

Lake ecosystems are deeply integrated into local and regional economies through recreation, tourism, and as sources of food and drinking water. Shifts in lake phytoplankton biomass, which are mediated by climate warming will alter these benefits with potential cascading effects on human well-being. The metabolic theory of ecology suggests that warming reduces lake phytoplankton biomass as basal metabolic costs increase, but this hypothesis has not been tested at the global scale. We use satellite-based estimates of lake surface temperature (LST) and lake surface chlorophyll-a concentration (chl-a; as a proxy for phytoplankton biomass) in 188 of the world’s largest lakes from 2002-2016 to test for interannual associations between chl-a and LST. In contrast to predictions from metabolic ecology, we found that LST and chl-a were positively correlated in 46% of lakes (p < 0.05). The associations between LST and chl-a depended on lake trophic state; warming tended to increase chl-a in phytoplankton-rich lakes and decrease chl-a in phytoplankton-poor lakes. We attribute the opposing responses of chl-a to LST to the effects of temperature on trophic interactions, and the availability of resources to phytoplankton. These patterns provide insights into how climate warming alters lake ecosystems on which millions of people depend for their livelihoods.

Estimating Surface Temperature of a Calibration Apparatus for Contact Surface Thermometers from Its Internal Temperature Profile

A calibration apparatus for contact surface thermometers was developed. Temperature of the upper surface of a copper cube of the calibration apparatus was used as reference surface temperature, which was estimated at around $$50\,{^{\circ }}\hbox {C}$$ , $$100\,{^{\circ }}\hbox {C}$$ , and $$150\,{^{\circ }}\hbox {C}$$ by not only two conventional industrial platinum resistance thermometers (IPRTs) but also five small-sized platinum resistance thermometers (SSPRTs) calibrated based on the International Temperature Scale of 1990 (ITS-90). These thermometers were inserted horizontally into the copper cube and aligned along the center axis of the copper cube. In the case of a no-load state without anything on the upper surface, the temperature profile inside the copper cube linearly decreased from the lower part to the upper surface, which suggests that the heat conduction inside the copper cube can be regarded as a one-dimensional steady state. On the other hand, in the case of a transient state just after the contact surface thermometer was applied to the upper surface, the temperature profile became a round shape. We obtained good agreement between the curvature of the temperature profiles and the results estimated by using an error function used for a one-dimensional transient heat conduction problem. The temperature difference between the estimated temperature by linear extrapolation using two IPRTs and that by extrapolation using the error function was within $$0.2\,{^{\circ }}\hbox {C}$$ in the transient state at around $$150\,{^{\circ }}\hbox {C}$$ . Over 10 min after the contact surface thermometer was applied, the temperature profile showed a linear shape again, which indicated that linear extrapolation using two IPRTs was well for the estimation of the reference surface temperature because the heat conduction state inside the copper cube came back to the one-dimensional steady state. Difference between the surface temperature and temperature detected by the contact surface thermometer was also observed after the contact surface thermometer touched on the upper surface. The difference was over $$0.1\,{^{\circ }}\hbox {C}$$ at several minutes after the contact surface thermometer touching on the reference surface and was suppressed with passing time in the transient state and became negligible over 10 min.

Estimation of Land Surface Temperature Using FengYun-2E (FY-2E) Data: A Case Study of the Source Area of the Yellow River

Land surface temperature (LST) is a key variable used for studies of water cycles and energy budgets of land–atmosphere interfaces. This study addresses the theory of LST retrieval from data acquired by the Chinese operational geostationary meteorological satellite FengYun-2E (FY-2E) in two thermal infrared channels (IR1: 10.29–11.45 μm and IR2: 11.59–12.79 μm) using a generalized split-window algorithm. Specifically, land surface emissivity (LSE) in the two thermal infrared channels is estimated from the LSE in channels 31 and 32 of the moderate-resolution imaging spectroradiometer (MODIS) product. In addition, an eight-day composition MODIS LSE product (MOD11A2) and the daily MODIS LSE product (MOD11A1) are used in the algorithm to estimate FY-2E emissivities. The results indicate that the LST derived from MOD11A1 is more accurate and, therefore, more appropriate for daily cloud-free LST estimation. Finally, the estimated LST was validated using the MODIS LST product for the heterogeneous source area of the Yellow River. The results show a significant correlation between the two datasets, with a correlation coefficient ( R ) varying from 0.60 to 0.94 and a root mean square error ranging from 1.89 to 3.71 K. Moreover, the estimated LST agrees well with ground-measured soil temperatures, with an R of 0.98.

A Call for New Approaches to Quantifying Biases in Observations of Sea Surface Temperature

Abstract Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications. Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method. New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.

Investigating the mechanisms of diurnal rainfall variability over Peninsular Malaysia using the non-hydrostatic regional climate model

This study aims to provide a basis for understanding the mechanisms of diurnal rainfall variability over Peninsular Malaysia by utilising the Non-Hydrostatic Regional Climate Model (NHRCM). The present day climate simulations at 5 km resolution over a period of 20 years, from 1st December 1989 to 31st January 2010 were conducted using the six-hourly Japanese re-analysis 55 years (JRA-55) data and monthly Centennial in situ Observation Based Estimates (COBE) of sea surface temperature as lateral and lower boundary conditions. Despite some biases, the NHRCM performed reasonably well in simulating diurnal rainfall cycles over Peninsular Malaysia. During inter-monsoon periods, the availability of atmospheric moisture played a major role in modulating afternoon rainfall maxima over the foothills of the Titiwangsa mountain range (FT sub-region). During the southwest monsoon, a lack of atmospheric moisture inhibits the occurrence of convective rainfall over the FT sub-region. The NHRCM was also able to simulate the suppression of the diurnal rainfall cycle over the east coast of Peninsular Malaysia (EC sub-region) and afternoon rainfall maximum over the Peninsular Malaysia inland-valley (IN sub-region) area during the northeast monsoon. Over the EC sub-region, daytime radiational warming of the top of clouds enhanced atmospheric stability, thus reducing afternoon rainfall. On the other hand, night-time radiational cooling from cloud tops decreases atmospheric stability and increases nocturnal rainfall. In the early morning, the rainfall maximum was confined to the EC sub-region due to the retardation of the north-easterly monsoonal wind by the land breeze and orographic blocking. However, in the afternoon, superimposition of the sea breeze on the north-easterly monsoonal wind strengthened the north-easterly wind, thus causing the zone of convection to expand further inland.

Estimating land surface temperature in ArcGIS using Landsat-8, Hoshangabad district, (Madhya Pradesh)

Landsat surface temperature (LST) can be applied for finding Global Warming, vegetation suitability, glacier, urban heat temperature monitoring and many more. Landsat 8 with a payload of new instrument called the Thermal Infrared Sensors (TIRS).This devices captures the Earth’s surface radiation through two bands, band 10 and band 11. The main purpose of this paper is to automate the surface temperature mapping through various ArcGIS geoprocessing tools in this paper we perform Land surface temperature estimation using SPLIT-WINDOW algorithm on Landsat 8 TIRS (Thermal Infrared Sensor) and OLI (Operational Land Imager) Sensor dataset of Hoshangabad District. Thermal Infrared Remote Sensor exhibit two thermal Bands 10 and 11.SPLIT- WINDOW algorithm require brightness temperature value of both band 10 and 11 as well as land surface emissivity calculated from OLI bands (NIR AND RED) for estimation of land surface temperature. The study also showed that there is a difference between the temperature values that has calculated from band 10 and band 11. From the resulted maps, the temperature of the North-Western side of the study area have the highest temperature values.

Evaluating the impact of adjusting surface temperature derived from Landsat 7 ETM+ in crop evapotranspiration assessment using high-resolution airborne data

ABSTRACTSurface temperature (Ts) is an essential parameter in many land surface processes. When Ts is obtained from remotely sensed satellite data the consideration of atmospheric correction may be needed to obtain accurate surface temperature estimates. Most atmospheric correction methods adjust atmospheric transmissivity, path radiance and downward thermal radiation coefficients. Following a standardized atmospheric correction of Landsat 7 thermal data, some differences were found between these corrected data and surface temperature derived from very-high resolution airborne thermal data. Five different methods for determining atmospheric correction were evaluated comparing atmospherically corrected Landsat 7 data with airborne data for an area of olive orchards located at Southern Spain. When using standard default Landsat 7 calibration coefficients Ts differences between satellite and airborne observations ranged from 1 to 6 K, highlighting the need to perform more robust atmospheric correction. When applying the customized values for semi-arid temperate climate in Idaho, USA, and the values based on the National Centers for Environmental Prediction (NCEP) Ts differences ranged from 1 to 4 K, indicating that additional local calibration may be appropriate. Optimal coefficients were determined using the Generalized Reduced Gradient (GRG) approach, a nonlinear algorithm included in Solver tool, obtaining Ts differences around 1–3 K. In order to evaluate the impact of considering the proposed correction approaches, assessment of the evapotranspiration and crop coefficient values derived from the Mapping Evapotranspiration with Internalized Calibration (METRIC) energy balance model provided maximum errors of around 4%, indicating that the METRIC model does not require a robust atmospheric correction. However, the localized calibration approaches are proposed as useful alternatives when absolute land surface temperatures values are required, as in the case of the determination of crop water stress based on differences between canopy (Tc) and air temperature (Tair).

Reconstructing dust storm frequency in China since 1617 CE, using coral-based estimates of sea surface temperature

Dust storms occur frequently in arid and semi-arid regions of China and other parts of the world, exerting a considerable influence on air quality in densely populated areas. Instrumental observations of dust storms are only available over the past 50 to 60years, limiting our ability to understand dust storm variability over longer timescales. However, tropical sea surface temperatures have been reconstructed over the past four centuries, using geochemical records from corals. Here we show that tropical Pacific sea surface temperatures, as recorded by corals, can be used to reconstruct dust storm frequency at one-year resolution. Based on the coral-reconstructed annual sea-surface temperature anomaly data from two regions (the western Pacific; 25°N–25°S, 110–155°E, and the eastern Pacific; 10°N–10°S, 175°E–85°W) published by Tierney et al. (2015), we reconstructed the frequency of dust storms in northern China (DSFCN) for the period from 1617 to 1953CE. The reconstructed DSFCN variation can be divided into several distinct periods: (1) DSFCN increased from 1617 to 1650, then (2) decreased from 1650 to 1675, (3) remained unchanged from 1675 to 1755, (4) increased again from 1755 to 1860, (5) remained unchanged from 1860 to 1925, and finally (6) decreased rapidly from 1925 to 1995. We propose the following causal chain to explain the observed relationship between DSFCN and SST: as tropical Pacific Ocean SST increases, the Siberian High weakens and the Eastern Asian Trough strengthens. As the air pressure difference weakens, the East Asian Winter Monsoon weakens, and strong wind frequency decreases, causing DSFCN to decrease as well. The statistical results support this interpretation of the causal chain.

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

A novel approach for the Raman measurement of nuclear materials is reported in this paper. It consists of the enclosure of the radioactive sample in a tight capsule that isolates the material from the atmosphere. The capsule can optionally be filled with a chosen gas pressurized up to 20 bars. The micro-Raman measurement is performed through an optical-grade quartz window. This technique permits accurate Raman measurements with no need for the spectrometer to be enclosed in an alpha-tight containment. It therefore allows the use of all options of the Raman spectrometer, like multi-wavelength laser excitation, different polarizations, and single or triple spectrometer modes. Some examples of measurements are shown and discussed. First, some spectral features of a highly radioactive americium oxide sample (AmO2) are presented. Then, we report the Raman spectra of neptunium oxide (NpO2) samples, the interpretation of which is greatly improved by employing three different excitation wavelengths, 17O doping, and a triple mode configuration to measure the anti-stokes Raman lines. This last feature also allows the estimation of the sample surface temperature. Finally, data that were measured on a sample from Chernobyl lava, where phases are identified by Raman mapping, are shown.

A Novel Technique for Raman Analysis of Highly Radioactive Samples Using Any Standard Micro-Raman Spectrometer

A novel approach for the Raman measurement of nuclear materials is reported in this paper. It consists of the enclosure of the radioactive sample in a tight capsule that isolates the material from the atmosphere. The capsule can optionally be filled with a chosen gas pressurized up to 20 bars. The micro-Raman measurement is performed through an optical-grade quartz window. This technique permits accurate Raman measurements with no need for the spectrometer to be enclosed in an alpha-tight containment. It therefore allows the use of all options of the Raman spectrometer, like multi-wavelength laser excitation, different polarizations, and single or triple spectrometer modes. Some examples of measurements are shown and discussed. First, some spectral features of a highly radioactive americium oxide sample (AmO2) are presented. Then, we report the Raman spectra of neptunium oxide (NpO2) samples, the interpretation of which is greatly improved by employing three different excitation wavelengths, 17O doping, and a triple mode configuration to measure the anti-stokes Raman lines. This last feature also allows the estimation of the sample surface temperature. Finally, data that were measured on a sample from Chernobyl lava, where phases are identified by Raman mapping, are shown.

Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate

In this paper, the negative impact of urbanization over a time and its effect on increasing trend of temperature and degradation of urban ecology was assessed using the Landsat thermal data and field survey of Lucknow city, India. Land surface temperature (LST) estimation has been carried out using Mono-window algorithm, temporal land use change map, assessment of vegetation cover through Normalized Difference Vegetation Index (NDVI), and ecological evaluation of the city was carried out using the Urban Thermal Field Variance Index (UTFVI). Results indicated that the spatial distribution of the land surface temperature was affected by the land use-land cover change and anthropogenic causes. The mean land surface temperature difference between the years 2002 and 2014 was found is 0.75°C. The observed results showed that the central portion of the city exhibited the highest surface temperature compared to the surrounding open area, the areas having dense built-up displayed higher temperatures and the areas covered by vegetation and water bodies exhibited lower temperatures. Strong correlation is observed between Land surface temperatures with Normalized Difference Vegetation Index (NDVI) and UTFVI. The observed LST of the area also validated trough the Google Earth Images. Ecological evaluation of the area also showed that the city has worst ecological index in the highly urbanized area in the central portion of the city. The present study provides very scientific information on impact of urbanization and anthropogenic activities which cause major changes on eco-environment of the city.

Inversion of AMSR‐E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature

AbstractInversions of the Earth Observation Satellite (EOS) Advanced Microwave Scanning Radiometer (AMSR‐E) brightness temperatures (Tbs) to derive the land surface temperature (Ts) are presented based on building a global transfer function by neural networks trained with AMSR‐E Tbs and retrieved microwave Ts*. The only required inputs are the Tbs and monthly climatological emissivities, minimizing the dependence on ancillary data. The inversions are accompanied by a coarse estimation of retrieval uncertainty, an estimate of the quality of the retrieval, and a series of flags to signal difficult inversion situations. For ∼75% of the land surface the root‐mean‐square difference (RMSD) between the training target Ts* and the neural network retrieved Ts is below 2.8 K. The RMSD when comparing with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear‐sky Ts is below 3.9 K for the same conditions. Over 10 ground stations, AMSR‐E and MODIS Ts were compared with the in situ data. Overall, MODIS agrees better with the station Ts than AMSR‐E (all‐station mean RMSD of 2.4 K for MODIS and 4.0 for AMSR‐E), but AMSR‐E provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analyzed stations. At many stations the RMSD of the AMSR‐E clear and cloudy sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric conditions. Closest agreement with the in situ Ts happens for stations with dense vegetation, where AMSR‐E emissivity is less varying.

Inversion of AMSR‐E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature

AbstractA comparison of land surface temperature (Ts) derived from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR‐E) with infrared Ts is presented. The infrared Ts include clear‐sky estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and Infrared Imager, the Geostationary Operational Environmental Satellite (GOES) Imager, and the Japanese Meteorological Imager. The higher discrepancies between AMSR‐E and MODIS are observed over deserts and snow‐covered areas. The former seems to be associated with Ts underestimation by MODIS, whereas the latter is mostly related to uncertainties in microwave emissivity over snow/ice. Ts differences between AMSR‐E and MODIS are significantly reduced after masking out snow and deserts, with a bias change from 2.6/4.6 K to 3.0/1.4 K for daytime/nighttime and a standard deviation (STD) decrease from 7.3/7.9 K to 5.1/3.9 K. When comparing with all infrared sensors, the STD of the differences between microwave and infrared Ts is generally higher than between IR retrievals. However, the biases between microwave and infrared Ts are, in some cases, of the same order as the ones observed between infrared products. This is the case for GOES, with daytime biases with respect to AMSR‐E and MODIS of 0.45 K and 0.60 K, respectively. While the infrared Ts are clear‐sky estimates, AMSR‐E also provides Ts under cloudy conditions. For frequently cloudy regions, this results in a large increase of available Ts estimates (&gt;250%), making the microwave Ts a very powerful complement of the infrared estimates.

New northwest Pacific radiolarian data as a tool to estimate past sea surface and intermediate water temperatures

A new radiolarian data set for transfer function estimates of past sea surface temperature (SST) and intermediate water temperature was developed in this study for the northwestern Pacific Ocean covering a region from 1° to 50°N and 120° to 167°E. We analyzed 87 sediments surface samples, selected 30 species and/or species group found in shallow water for estimating past summer SST and 17 species and/or species group found in the intermediate water for estimating past intermediate water temperature. Since the intermediate water temperature changes greatly between 200 and 500 m, our estimates provide values at 500 m because temperatures are relatively stable between 500 and 1000 m. In this context, we estimated past summer SST and intermediate water (at ~500 m) temperature within an error margin of 0.9 and 1.2°C, respectively. A test of the accuracy of our transfer functions, conducted on core samples provided by IODP Expedition 346 Site U1429 in the northern East China Sea, showed that the reconstructed summer SSTs fluctuated between 17.2 and 26.5°C in selected late Pleistocene sequences. These temperatures corresponded to modern winter and summer SST, respectively, which highlights the ability of our new database to accurately reconstruct summer SST. The reconstructed intermediate water temperature fluctuates between 3 and 8°C, which corresponds to the observed temperature range at depths of ~500 m at high and midlatitudes, respectively.

Experimental and numerical simulation studies on heat transfer to calorimeters engulfed in diesel pool fires

Characterization of heat transfer to calorimeters engulfed in pool fires is extremely important. To estimate the heat flux to the calorimeters, experiments are performed with horizontal stainless steel 304L pipes engulfed in diesel pool fires. The concept of adiabatic surface temperature is applied to predict the incident heat flux to horizontally oriented calorimeters engulfed in diesel pool fires. Plate thermometers are used to measure the adiabatic surface temperature for diesel pool fires. The estimated subsurface temperatures inside the steel pipes using the adiabatic surface temperature concept and the measured temperatures are in good agreement. Adiabatic surface temperature is also computed from fire simulations. The incident heat fluxes to the steel pipes engulfed in fire predicted from the simulations are found to be in good agreement with the experiments. The fire numerical code is validated against the 1 m pool fire experimental results of centerline temperature distribution and irradiances away from fire. A correlation is provided for the estimation of adiabatic surface temperature for large diesel pool fires. These results would provide an effective way for thermal test simulations.

Expanded Florida reef development during the mid-Pliocene warm period

The coral fauna of the Tamiami Formation documents a northern expansion of reef development along the Florida Peninsula during the mid-Pliocene warm period (MPWP). Radiometric dating (U-Pb) of Solenastrea bournoni produced an age of 2.99±0.11Ma, constraining reef development to the MPWP and the peak of Plio-Pleistocene faunal turnover; subsequent to the final closure of the Central American Seaway (CAS) but prior to major Northern Hemisphere Glaciation (NHG). Coral faunal analyses are based on a total of 1614 coral specimens collected along a 165km stretch of the west Florida coast, and included rarefaction and detrended correspondence analysis (DCA). A total of 60 coral species occur within the Tamiami Formation, with faunal assemblages ranging from 42 to 87% extinct taxa. The Tamiami collections can be split into a southern “reef” assemblage with high diversity of stenotopic taxa and a northern “non-reef” assemblage with lower diversity eurytopic taxa. The southern reef assemblage contains framework buildups of the dominant tropical taxa Stylophora affinis, Orbicella annularis, and Acropora cervicornis. We interpret enhanced west Florida reef development during the middle Pliocene to be a product of more equitable sea surface temperatures, and reduced salinity fluctuations associated with higher sea levels. While mean sea surface temperature estimates based on oxygen isotopic analysis of the coral Solenastrea bournoni (25.3°C) are similar to present day values (26°C), a completely flooded southern Florida Platform in the Pliocene would be less prone to salinity fluctuations associated with coastal runoff and extreme cold-water events during winter storms. While higher latitude range shifts of tropical reef corals associated with current global climate change have been documented elsewhere in the world, we do not foresee the West Florida Shelf being conducive to significant range shifts in tropical coral taxa or reef development within the coming century.

Preliminary Test of a Data Assimilation System with a Regional High-Resolution Atmosphere–Ocean Coupled Model Based on an Ensemble Kalman Filter

AbstractAn ensemble Kalman filter (EnKF) that uses a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimates and to represent the uncertainties of SST in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which time a tropical cyclone (TC) as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model reproduced SST distributions realistically even without assimilating SST and sea surface salinity observations, and atmospheric variables provided to ocean models can, therefore, control oceanic variables physically to some extent. The forecast error covariance calculated in the EnKF with the coupled model showed dependency on oceanic vertical mixing for near-surface atmospheric variables due to the difference of variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. The EnKF with the coupled model reproduced the intensity change of Typhoon Halong (2014) during the mature phase more realistically than with an uncoupled atmosphere model, although there remained a degradation of the SST estimate, particularly around the Kuroshio region. This suggests that an atmosphere–ocean coupled data assimilation system should be developed that is able to physically control both atmospheric and oceanic variables.

Adding value to INSAT-3D sea surface temperature fields using MODIS data over the tropical Indian Ocean

ABSTRACTAccurate estimates of sea surface temperature (SST) are crucial for climate studies, numerical weather prediction and air–sea interactions. Following the launch of India’s advanced geostationary satellite – INSAT-3D with two thermal infrared split window channels in 2013, it is now possible to monitor land and ocean surfaces more reliably at higher spatiotemporal scale. In this article, an attempt has been made to develop a more accurate infrared-based cloud-free SST estimates over the tropical Indian Ocean by the synergistic use of geostationary INSAT-3D and polar-orbiting Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements through a successive correction method. This method is applied primarily for the month of May 2015 at coarser spatial resolution and at a daily scale. Results are compared independently with multi-satellite SST estimates and also against in situ observations such as Argo floats and buoys. The merged SST product shows noticeable improvement over INSAT-3D-based estimates alone. Comparison of the merged SST product with Argo observations shows that the root mean square difference (RMSD) has been improved from 1.23 to 0.79 K, and bias and correlation are also significantly improved. Overall results indicate that the synergistic use of INSAT-3D and MODIS satellite observations has potential for more accurate SST estimation over the tropical Indian Ocean at finer temporal resolution and larger spatial coverage for several near-real time meteorological and oceanographic applications.