Surface Atmospheric Variables Research Articles

Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

Abstract CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state. Significance Statement Snowfall is a critical contributor to the global water–energy budget, with important connections to water resource management, flood mitigation, and ecosystem sustainability. However, traditional spaceborne remote monitoring of snowfall faces challenges due to a near-surface radar blind zone, which masks a portion of the atmosphere. In this study, a deep learning model was developed to fill in missing data across these regions using surface radar and atmospheric state variables. The model accurately predicts reflectivity, with significant improvements over conventional methods. This innovative approach enhances our understanding of reflectivity patterns and atmospheric interactions, bolstering advances in remote snowfall prediction.

Spatiotemporal Convolutional Approach for the Short-Term Forecast of Hourly Heavy Rainfall Probability Integrating Numerical Weather Predictions and Surface Observations

Abstract The accurate prediction of short-term rainfall, and in particular the forecast of hourly heavy rainfall (HHR) probability, remains challenging for numerical weather prediction (NWP) models. Here, we introduce a deep learning (DL) model, PredRNNv2-AWS, a convolutional recurrent neural network designed for deterministic short-term rainfall forecasting. This model integrates surface rainfall observations and atmospheric variables simulated by the Precision Weather Analysis and Forecasting System (PWAFS). Our DL model produces realistic hourly rainfall forecasts for the next 13 h. Quantitative evaluations show that the use of surface rainfall observations as one of the predictors achieves higher performance (threat score) with 263% and 186% relative improvements over NWP simulations for the first 3 h and the entire forecast hours, respectively, at a threshold of 5 mm h−1. Noting that the optical-flow method also performs well in the initial hours, its predictions quickly worsen in the final hours compared to other experiments. The machine learning model, LightGBM, is then integrated to classify HHR from the predicted hourly rainfall of PredRNNv2-AWS. The results show that PredRNNv2-AWS can better reflect actual HHR conditions compared with PredRNNv2 and PWAFS. A representative case demonstrates the superiority of PredRNNv2-AWS in predicting the evolution of the rainy system, which substantially improves the accuracy of the HHR prediction. A test case involving the extreme flood event in Zhengzhou exemplifies the generalizability of our proposed model. Our model offers a reliable framework to predict target variables that can be obtained from numerical simulations and observations, e.g., visibility, wind power, solar energy, and air pollution.

Towards Accurate Radiometric Calibration of INSAT-3D and INSAT-3DR IMAGER: Addressing Uncertainty and Error Sources

ABSTRACT The proper radiometric calibration of the INSAT-3D and −3DR IMAGER is of utmost importance for effective weather forecasting and monitoring. Accurate and reliable images are crucial for predicting weather patterns, tracking storms, and assessing the impact of natural disasters. This research paper presents an evaluation of the radiometric calibration of the INSAT-3D and −3DR IMAGER using a reflectance-based method referenced to in-situ ground site measurements. Calibration campaigns were conducted at the Great Rann of Kutch (GROK) in Gujarat, India during February 2022 to ensure synchronous measurements of surface reflectance and atmospheric variables. In this study, the radiative transfer model called 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) was employed to simulate the Top-Of-Atmospheric (TOA) radiance. The calibration coefficients for the Visible (VIS) and Short Wave Infrared (SWIR) bands of the IMAGER were determined by comparing the TOA radiance obtained from the radiative transfer model with the Level 1B product of satellite data. The results convincingly demonstrate that the reflectance-based calibration method yields highly accurate and reliable radiometric calibration for both the VIS and SWIR bands of the INSAT-3D and −3DR IMAGER, with an uncertainty significantly smaller than the expected sensor degradation. Specifically, it was found that the VIS band of INSAT-3D underestimates the radiance value by 9.05%, while the VIS band of INSAT-3DR underestimates it by 4.88%. The study also discusses various sources of uncertainty and emphasizes the need for comprehensive uncertainty quantification to ensure the radiometric accuracy of the IMAGER data. Overall, the calibration results indicate that the absolute radiometric accuracy for the VIS and SWIR bands of the IMAGER on-board INSAT-3D and INSAT-3DR satellites is better than 4%. This research contributes to enhancing the reliability and usability of the INSAT-3D and −3DR IMAGER data for weather forecasting and monitoring applications.

Extreme marine heatwaves and cold-spells events in the Southern North Sea: classifications, patterns, and trends

In this study, we examined the long-term spatiotemporal trend of marine heatwaves (MHW) and marine cold spells (MCS) characteristics in the southern North Sea over the last four decades (1982-2021). We then estimated the difference between their annual mean values and the possible relationship with the large-scale climate modes of natural sea surface temperature (SST) and atmospheric variability using satellite SST data. The SST warming rate was 0.33 ± 0.06°C/decade and was associated with an increase in MHW frequency (0.85 ± 0.39 events/decade) and a decrease in MCS frequency (-0.92 ± 0.40 events/decade) over the entire period. We found a distinct difference between the annual mean values of MHW and MCS characteristics, with a rapid increase in total MHW days (14.36 ± 8.16 days/decade), whereas MCS showed an opposite trend (-16.54 ± 9.06 days/decade). The highest MHW frequency was observed in the last two decades, especially in 2014 (8 events), 2020 (5 events), and 2007 (4 events), which were also the warmest years during the study period. Only two years (2010 and 2013) in the last two decades had higher MCS frequency, which was attributed to the strong negative phase of the North Atlantic Oscillation (NAO). Our results also show that on the annual scale, both the East Atlantic Pattern (EAP) and the Atlantic Multidecadal Oscillation (AMO) play a more important role in the formation of the MHW in the southern North Sea than the other teleconnections (e.g., the NAO). However, the NAO made the largest contribution only in the winter. Strong significant (p < 0.05) positive/negative correlations were found between oceanic and atmospheric temperatures and the frequency of MHW/MCS. This suggests that with global warming, we can expect an increase/decrease in MHW/MCS occurrences in the future.

Behavior of the ITCZ second band near the Peruvian coast during the 2017 coastal El Niño

The behavior of the second band of the Intertropical Convergence Zone (ITCZ) near the Peruvian coast during early 2017 is studied, using precipitation, surface winds, sea surface temperature (SST) and atmospheric variables in different isobaric levels. The proposal of a daily index (Ia) to identify opportunely the formation of this band and the Lorenz energy terms in the region is also considered. This band was present from late January to early April 2017, associated with an anomalous dipole of sea level pressure between the east and west eastern Equatorial Pacific that configured anomalously northerly surface winds and the release of southeasterly trade winds near Peru. In medium levels, a zonally oriented positive mixing ratio anomaly was observed in early March over the ITCZ second band, associated with heavy rain systems over the northern Peruvian coastal region. In the same period, positive anomalies of divergence in high tropospheric levels were observed. The daily Ia index allowed an effective detection of the ITCZ second band 11 days before the maximum coastal precipitation, and the Lorenz energy terms showed eddy kinetic energy (KE) peaks in January and February and a contribution of barotropic instability in equatorial regions.

How morning soil moisture conditions influence afternoon precipitation events in South America

AbstractThe land–atmosphere interactions play an important role in the South American climate system. Previous studies have explored the coupling between soil moisture (SM), surface heat fluxes and atmospheric variables such as temperature and precipitation; however, the SM–precipitation feedback is still debated, especially at the diurnal scale. We computed three diurnal local coupling diagnostics between spatial, temporal and heterogeneity morning SM anomalies and the occurrence of afternoon precipitation events. We analyse South America in the period October–March 1983–2012 with the ERA5 reanalysis and the RCA4 regional climate model. Two 30‐year climate simulations are performed: a control and an uncoupled simulation where the SM is prescribed with daily climatological values, limiting the SM–atmosphere interaction. Comparing both simulations we isolate the effect of SM variability, allowing us to confirm that the diagnostics are indeed showing the contribution of the SM–precipitation coupling. We test different methods of calculating the morning SM anomalies and observe that all diagnostics show some methodological sensitivity, especially the spatial and temporal diagnostics. Despite the differences, in eastern Brazil we find a preference for afternoon precipitation to develop when the soil is wetter and heterogeneous, over points that are wetter than their surroundings. Towards southeastern South America, there is a spatial and temporal preference for drier or average conditions, depending on the method, with heterogeneity preference, and the same is observed in southwestern Amazonia in RCA4. Ultimately, it is important that future studies disentangle the spatiotemporal coupling mechanisms that may act simultaneously into its distinct components to avoid erroneous assumptions and contradictory results.

Effect of data assimilation in the Polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard

Although the Polar Weather Research and Forecasting (PWRF) model has been developed for polar environments, simulations of atmospheric states in the polar region still have large uncertainties. Therefore, the effects of data assimilation (DA) to improve forecasts in the polar region were investigated for September 2017 using PWRF and the three-dimensional variational (3DVAR) DA method. The experiments without DA and those with DA assimilating only conventional observations and both conventional observations and satellite radiance data were performed. The forecasts from all experiments both without and with DA underestimated (overestimated) the downward longwave (shortwave) radiation flux due to the underestimation of the amount of Arctic clouds. When satellite radiance data (i.e., the Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS)) were assimilated in addition to conventional observations in the PWRF, the distribution and amount of water vapor became closer to observations, which improves cloud liquid water forecasts. Therefore, when both conventional observations and radiance data were assimilated, the 25–30 h forecast errors of the downward longwave and shortwave radiation fluxes and sensible and latent heat fluxes decreased by 12.7%, 8.1%, 3.2%, and 7.8% with the WSM5 scheme and by 17.1%, 4.7%, 2.5%, and 3.1% with the 2-moment Morrison scheme, respectively, compared to those in the experiments without DA. The forecast errors of the 10 m wind and 2 m temperature with DA were smaller than those without DA at most observation stations. Therefore, the uncertainties of the Arctic forecasts in the PWRF decreased when using DA. To further improve Arctic forecasts, the assimilation of various additional satellite data is necessary.

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs

More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ∼175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.

Exploring the Pacific Arctic Seasonal Ice Zone With Saildrone USVs.

More high-quality, in situ observations of essential marine variables are needed over the seasonal ice zone to better understand Arctic (or Antarctic) weather, climate, and ecosystems. To better assess the potential for arrays of uncrewed surface vehicles (USVs) to provide such observations, five wind-driven and solar-powered saildrones were sailed into the Chukchi and Beaufort Seas following the 2019 seasonal retreat of sea ice. They were equipped to observe the surface oceanic and atmospheric variables required to estimate air-sea fluxes of heat, momentum and carbon dioxide. Some of these variables were made available to weather forecast centers in real time. Our objective here is to analyze the effectiveness of existing remote ice navigation products and highlight the challenges and opportunities for improving remote ice navigation strategies with USVs. We examine the sources of navigational sea-ice distribution information based on post-mission tabulation of the sea-ice conditions encountered by the vehicles. The satellite-based ice-concentration analyses consulted during the mission exhibited large disagreements when the sea ice was retreating fastest (e.g., the 10% concentration contours differed between analyses by up to ~175 km). Attempts to use saildrone observations to detect the ice edge revealed that in situ temperature and salinity measurements varied sufficiently in ice bands and open water that it is difficult to use these variables alone as a reliable ice-edge indicator. Devising robust strategies for remote ice zone navigation may depend on developing the capability to recognize sea ice and initiate navigational maneuvers with cameras and processing capability onboard the vehicles.

Coupled Ocean–Atmosphere Covariances in Global Ensemble Simulations: Impact of an Eddy-Resolving Ocean

AbstractPatterns of correlations between the ocean and the atmosphere are examined using a high-resolution (1/12° ocean and ice, 1/3° atmosphere) ensemble of data assimilative, coupled, global, ocean–atmosphere forecasts. This provides a unique perspective into atmosphere–ocean interactions constrained by assimilated observations, allowing for the contrast of patterns of coupled processes across regions and the examination of processes affected by ocean mesoscale eddies. Correlations during the first 24 h of the coupled forecast between the ocean surface temperature and atmospheric variables, and between the ocean mixed layer depth and surface winds are examined as a function of region and season. Three distinct coupling regimes emerge: 1) regions characterized by strong sea surface temperature fronts, where uncertainty in the ocean mesoscale influences ocean–atmosphere exchanges; 2) regions with intense atmospheric convection over the tropical oceans, where uncertainty in the modeled atmospheric convection impacts the upper ocean; and 3) regions where the depth of the seasonal mixed layer (MLD) determines the magnitude of the coupling, which is stronger when the MLD is shallow and weaker when the MLD is deep. A comparison with models at lower horizontal (1/12° vs 1° and 1/4°) and vertical (1- vs 10-m depth of the first layer) ocean resolution reveals that coupling in the boundary currents, the tropical Indian Ocean, and the warm pool regions requires high levels of horizontal and vertical resolution. Implications for coupled data assimilation and short-term forecasting are discussed.

The North Atlantic High-Resolution Regional Climate Model Experiment for Oceanic and Atmospheric Applications

We present a high-resolution historical analysis of the atmosphere over the North Atlantic for the period from January 1979 to December 2018. The dataset was obtained using the nonhydrostatic atmospheric model WRF-ARW version 3.8.1 and contains two experiments that differ in spatial resolution: 14 km (HiRes) and 77 km (LoRes). Except for the spatial resolution, the configuration remained identical in both experiments: 50 vertical sigma-levels (starting from ~12 m up to 50 hPa); ERA-Interim reanalysis was used as forcing and spectral nudging. The dataset for HiRes and LoRes experiments was named NAAD (North Atlantic Atmospheric Downscaling). Preliminary results of both HiRes and LoRes reveal good agreement with observations and the ERA-Interim, ERA5, and ASRv2 reanalyses. The NAAD dataset is available online at http://www.naad.ocean.ru and provides a variety of surface and upper-troposphere atmospheric variables with a time step of 3 h.

Modification of surface energy balance during springtime: The relative importance of biophysical and meteorological changes

In ecosystems characterized by strong seasonality in leaf area, the emergence of leaves during springtime modifies land surface energy balance by altering surface biophysical properties during a period when atmospheric conditions are also changing. However, the relative importance and interactions among surface biophysical and atmospheric variables in modifying the surface energy balance are not well understood. In this study, we use a physically-based attribution method to quantify the relative importance of covarying surface biophysical and atmospheric variables in modifying the surface energy balance during springtime. Results show that the widely observed decrease in the Bowen ratio that occurs with leaf emergence is not solely attributable to the sharp decrease in surface resistance caused by increasing leaf area. Rather, decreases in the Bowen ratio reflect the combined effects of changes in surface properties and atmospheric conditions. Specifically, decreasing surface resistance and increasing air temperature both act to reduce the Bowen ratio, while concurrent increases in specific humidity provide a negative feedback that constrains evaporative fluxes. In parallel, aerodynamic resistance tends to increase after leaf emergence largely because wind speed tends to decrease during springtime. These findings provide a refined characterization of surface energy balance dynamics during springtime when both surface and atmospheric conditions are changing rapidly and reveal previously understudied properties of the near-surface atmosphere that influence surface Bowen ratio and aerodynamic resistance.

Dominant Modes of China Summer Heat Waves Driven by Global Sea Surface Temperature and Atmospheric Internal Variability

AbstractThis study applies the maximum temperatures at more than 2000 Chinese stations to investigate the dominant modes of China summer heat waves (HWs). The first empirical orthogonal function (EOF) mode of the HW days reflects an increased frequency of HWs in northern China (NC), while the second and third modes represent two distinct interannual modes, with key regions over the Yangtze River valley (YRV) and southern China (SC), respectively. The NC HWs are possibly associated with the Atlantic–Eurasian teleconnection, showing zonally propagating wave trains over the North Atlantic and Eurasian continent. The YRV HWs are proposed to be linked to the North Atlantic Oscillation, which may trigger a southeastward-propagating wave train over northern Russia and East Asia that results in a high pressure anomaly over the YRV. The SC HWs are obviously dominated by the Indian Ocean and northwest Pacific warm SSTs owing to the transition from the preceding El Niño to La Niña, which excites above-normal highs over SC. The anomalously high pressures over NC, the YRV, and SC are usually accompanied by descending air motions, clear skies, decreased precipitation, and increased solar radiation, which jointly cause a drier and hotter soil condition that favors the emergence of HWs. The GFDL HiRAM experiments are able to reproduce the historical evolution of NC and SC HWs, but fail to capture the YRV HWs. The correlation coefficient between model PC1 (PC2) and observed PC1 (PC3) for the period of 1979–2008 is 0.65 (0.38), which significantly exceeds the 95% (90%) confidence level, indicating that this model has a more faithful representation for the SST-forced HWs.

Putting It All Together: Adding Value to the Global Ocean and Climate Observing Systems With Complete Self-Consistent Ocean State and Parameter Estimates

In 1999, the consortium for Estimating the Circulation and Climate of the Ocean (ECCO) set out to synthesize the hydrographic data collected by the World Ocean Circulation Experiment (WOCE) and satellite sea surface height measurements into a complete and coherent description of the ocean afforded by an ocean general circulation model. Twenty years later, the versatility of ECCO's estimation framework enables production of global and regional ocean and sea-ice state estimates that incorporate not only the initial suite of data and its successors, but nearly all data streams available today. New observations include measurements from Argo floats, marine mammal-based hydrography, satellite retrievals of ocean bottom pressure and sea surface salinity, and ice-tethered profiler data in polar regions. The framework also produces improved estimates of uncertain inputs, including initial conditions, surface atmospheric state variables, and mixing parameters. The freely available state estimates and related efforts are property-conserving, allowing closed budget calculations that are a requisite to detect, quantify, and understand the evolution of climate-relevant signals as mandated by the Coupled Model Intercomparison Project Phase 6 (CMIP6) protocol. The solutions can be reproduced by users through provision of the underlying modeling and assimilation machinery. Regional efforts have spun off that offer increased spatial resolution to better resolve relevant processes. Emerging foci of ECCO are on global sea level change, in particular contributions from polar ice sheets, and the increased use of biogeochemical and ecosystem data to constrain global cycles of carbon, nitrogen and oxygen. Challenges in the coming decade include provision of uncertainties, informing observing system design, globally increased resolution, and moving toward coupled Earth system estimation with consistent momentum, heat and freshwater fluxes between the ocean, atmosphere, cryosphere and land.

Atmospheric Origins of Variability in the South Atlantic Meridional Overturning Circulation

Abstract Insights from the RAPID–MOCHA observation network in the North Atlantic have motivated a recent focus on the South Atlantic, where water masses are exchanged with neighboring ocean basins. In this study, variability in the South Atlantic meridional overturning circulation (SAMOC) at 34°S is attributed to global atmospheric forcing using an inverse modeling approach. The sensitivity of the SAMOC to atmospheric state variables is computed with the adjoint of the Massachusetts Institute of Technology general circulation model, which is fit to 20 years of observational data in a dynamically consistent framework. The dynamical pathways highlighted by these sensitivity patterns show that the domain of influence for the SAMOC is broad, covering neighboring ocean basins even on short time scales. This result differs from what has previously been shown in the North Atlantic, where Atlantic meridional overturning circulation (AMOC) variability is largely governed by dynamics confined to that basin. The computed sensitivities are convolved with surface atmospheric state variability from ERA-Interim to attribute the influence of each external forcing variable (e.g., wind stress, precipitation) on the SAMOC from 1992 to 2011. Here, local wind stress perturbations are shown to dominate variability on seasonal time scales while buoyancy forcing plays a minor role, confirming results from past forward perturbation experiments. Interannual variability, however, is shown to have originated from remote locations across the globe, including a nontrivial component originating from the tropical Pacific. The influence of atmospheric forcing emphasizes the importance of continuous widespread observations of the global atmospheric state for attributing observed AMOC variability.

Ensemble-Based Atmospheric Reanalysis Using a Global Coupled Atmosphere–Ocean GCM

ABSTRACT Ensemble-based atmospheric data assimilation (DA) systems are sometimes afflicted with an underestimation of the ensemble spread near the surface caused by the use of identical boundary conditions for all ensemble members and the lack of atmosphere–ocean interaction. To overcome these problems, a new DA system has been developed by replacing an atmospheric GCM with a coupled atmosphere–ocean GCM, in which atmospheric observational data are assimilated every 6 h to update the atmospheric variables, whereas the oceanic variables are subject to no direct DA. Although SST suffers from the common biases among many coupled GCMs, two months of a retrospective analysis–forecast cycle reveals that the ensemble spreads of air temperature and specific humidity in the surface boundary layer are slightly increased and the forecast skill in the midtroposphere is rather improved by using the coupled DA system in comparison with the atmospheric DA system. In addition, surface atmospheric variables over the tropical Pacific have the basinwide horizontal correlation in ensemble space in the coupled DA system but not in the atmospheric DA system. This suggests the potential benefit of using a coupled GCM rather than an atmospheric GCM even for atmospheric reanalysis with an ensemble-based DA system.

Observations of Local Meteorological Variability under Large-Scale Circulation Patterns over Athens, Greece

Linking synoptic circulation patterns to specific environmental problems is of significance in the Eastern Mediterranean region, which is characterized by increased seasonal climatic variability and a wealth of distinct weather patterns. This study aims to discuss the links between synoptic scale circulation, intra-day variability and sub-hourly temperature changes over Athens. Diurnal cycles of surface atmospheric variability were examined by applying Principal Component Analysis and Integral Quantities Analysis to a four months data set with surface meteorological elements. Sub-hourly temperature changes were identified by applying a simple linear technique. Principal Components, Integral Quantities and temperature change rates (geometric structures) were related with synoptic circulation categories. It was found that the presence of a Closed Low over the area results in intense along-mountain flows, whilst, after the passage of a trough, when a strong northwesterly flow is established over the area, surface recirculation flows develop. On 64% of the days, geometric structures were observed in the hourly temperature time-series, and they were found to occur across all synoptic situations. Cliff—ramps was the most common geometric structure, and step changes were found to be related with recirculation flows.

Day-1 INSAT-3DR Vicarious Calibration Using Reflectance-Based Approach Over Great Rann of Kutch

This study describes the post-launch calibration for visible (VIS) and shortwave infrared (SWIR) bands of Indian National Satellite System (INSAT)-3DR imager over Great Rann of Kutch (GROK) on Day-1 (15th September 2016), when the first time INSAT-3DR Imager camera was switched on. In order to account the characterization of errors and undetermined post-launch changes in sensor spectral response, this calibration activity was performed and extended for its monitoring to Day-56 (since the Day-1; 09th November 2016). A reflectance based technique is used in the present study. The surface reflectance and atmospheric variables were measured over the site as per solar and viewing geometry of the INSAT-3D scan. Top of atmosphere (TOA) spectral radiances were computed using 6SV (second simulation of the satellite signal in the solar spectrum) radiative transfer code with the in situ measurements as well as spectral response function of each channel. Preliminary results of the Day-1 vicarious calibration yield gain coefficients of 0.974 and 0.820 for VIS and SWIR channels respectively despite the inhomogeneity of the ground target caused by sufficient sub-surface soil moisture. In extension of the present study, the obtained gain coefficients were 1.001 and 0.9887 for VIS and SWIR, respectively, during Day-56 which indicates the performance of sensor is within the range of pre-launch laboratory calibration.

Using ARM Observations to Evaluate Climate Model Simulations of Land‐Atmosphere Coupling on the U.S. Southern Great Plains

AbstractSeveral independent measurements of warm‐season soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of land‐atmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operated in both a free‐running mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its free‐running counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. These results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.

Role of Pacific trade winds in driving ocean temperatures during the recent slowdown and projections under a wind trend reversal

Interannual to decadal variability in the Pacific Ocean is a prominent feature of Earth’s climate system, with global teleconnections. Recent studies have identified Pacific decadal variability as a major driver of periods of rapid and slower global mean surface air temperature change. Here, we use an eddy-permitting global ocean model to investigate the role of the observed 1992–2011 trade wind intensification and concurrent trends in surface atmospheric variables over the Pacific associated with the negative phase of the Interdecadal Pacific Oscillation (IPO) in driving ocean circulation and heat content changes. We find a strengthening of the Equatorial Undercurrent (EUC) in response to strengthened winds, which brings cooler water to the surface of the eastern Pacific and an increase in the Pacific shallow overturning cells (PSOC), which in turn drives additional heat into the subsurface western Pacific. The wind acceleration also results in an increase in the strength and subsequent heat transport of the Indonesian throughflow (ITF), which transports some of the additional heat from the western Pacific into the Indian Ocean. The circulation changes result in warming of the subsurface western Pacific, cooling of the upper eastern Pacific Ocean and warming of the subsurface Indian Ocean, with an overall increase in Indo-Pacific heat content. Further experiments impose a symmetric reversal of the atmospheric state to examine how the ocean would behave if the winds (and other atmospheric variables) were to revert to their initial state. This mimics a return to the neutral phase of the IPO, characterised by a weakening of the Pacific trade winds. In response we find a slowdown of the EUC and the PSOC, which results in a return to climatological SST conditions in the western and eastern Pacific. The ITF also slows towards its original strength. However, the subsurface temperature, heat content and ITF responses are not symmetric due to an overall increase in the surface heat flux into the ocean associated with the cooler surface of the Pacific. There may also be irreversible heat transport across the thermocline via diapycnal mixing, further contributing to this asymmetry. The net result of the experiment is that the Indo-Pacific subsurface ocean is warmer than it was in its initial state.