IMDAA Rani, S. Indira; Arulalan, T.; George, John P. ...
Journal of climate,
06/2021, Letnik:
34, Številka:
12
Journal Article
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A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its ...variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.
The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and ...Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).
The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), updates NASA’s previous satellite-era (1980 onward) reanalysis system to include additional observations and ...improvements to the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. As a major step toward a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground-and space-based remote sensing platforms. Here, in the first of a pair of studies, the MERRA-2 aerosol assimilation is documented, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. Initial validation and evaluation of the analyzed AOD fields are provided using independent observations from ground, aircraft, and shipborne instruments. The positive impact of the AOD assimilation on simulated aerosols is demonstrated by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. After showing the AOD evaluation, this paper takes a first look at aerosol–climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. The companion paper (Part II) evaluates and validates available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g., aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.
Abstract
Japan’s new geostationary satellite Himawari-8, the first of a series of the third-generation geostationary meteorological satellites including GOES-16, has been operational since July 2015. ...Himawari-8 produces high-resolution observations with 16 frequency bands every 10 min for full disk, and every 2.5 min for local regions. This study aims to assimilate all-sky every-10-min infrared (IR) radiances from Himawari-8 with a regional numerical weather prediction model and to investigate its impact on real-world tropical cyclone (TC) analyses and forecasts for the first time. The results show that the assimilation of Himawari-8 IR radiances improves the analyzed TC structure in both inner-core and outer-rainband regions. The TC intensity forecasts are also improved due to Himawari-8 data because of the improved TC structure analysis.
The complex features of rainfall diurnal cycles at the south China coast are examined using hourly rain gauge data and satellite products (CMORPH and TRMM 3B42) during 1998–2014. It is shown that ...morning rainfall is pronounced near the coasts and windward mountains, with high rainfall in the summer monsoon season, while afternoon rainfall is dominant on land, and nocturnal rainfall occurs at northern inland sites. Both satellite products report less morning rainfall and more afternoon rainfall than the rain gauge data, and they also miss the midnight rainfall minimum. These errors are mainly attributable to an underestimation of morning moderate and intense rains at coasts and an overestimation of afternoon–evening light rains on land. With a correction of the systematic bias, satellite products faithfully resolve the spatial patterns of normalized rainfall diurnal cycles related to land–sea contrast and terrains, suggesting an improved data application for regional climate studies. In particular, they are comparable to the rain gauge data in showing the linear reduction of morning rainfall from coasts to inland regions. TRMM is marginally better than CMORPH in revealing the overall features of diurnal cycles, while higher-resolution CMORPH captures more local details. All three datasets also present that morning rainfall decreases from May–June to July–August, especially on land; it exhibits pronounced interannual variations and a decadal increase in 1998–2008 at coasts. Such long-term variations of morning rainfall are induced by the coastal convergence and mountain liftings of monsoon shear flow interacting with land breeze, which is mainly regulated by monsoon southwesterly winds in the northern part of the South China Sea.
Abstract
Barents Sea Water (BSW) is formed from Atlantic Water that is cooled through atmospheric heat loss and freshened through seasonal sea ice melt. In the eastern Barents Sea, the BSW and ...fresher, colder Arctic Water meet at the surface along the Polar Front (PF). Despite its importance in setting the northern limit of BSW ventilation, the PF has been poorly documented, mostly eluding detection by observational surveys that avoid seasonal sea ice. In this study, satellite sea surface temperature (SST) observations are used in addition to a temperature and salinity climatology to examine the location and structure of the PF and characterize its variability over the period 1985–2016. It is shown that the PF is independent of the position of the sea ice edge and is a shelf slope current constrained by potential vorticity. The main driver of interannual variability in SST is the variability of the Atlantic Water temperature, which has significantly increased since 2005. The SST gradient associated with the PF has also increased after 2005, preventing sea ice from extending south of the front during winter in recent years. The disappearance of fresh, seasonal sea ice melt south of the PF has led to a significant increase in BSW salinity and density. As BSW forms the majority of Arctic Intermediate Water, changes to BSW properties may have far-reaching impacts for Arctic Ocean circulation and climate.
Promising new opportunities to apply artificial intelligence (AI) to the Earth and environmental sciences are identified, informed by an overview of current efforts in the community. Community input ...was collected at the first National Oceanic and Atmospheric Administration (NOAA) workshop on “Leveraging AI in the Exploitation of Satellite Earth Observations and Numerical Weather Prediction” held in April 2019. This workshop brought together over 400 scientists, program managers, and leaders from the public, academic, and private sectors in order to enable experts involved in the development and adaptation of AI tools and applications to meet and exchange experiences with NOAA experts. Paths are described to actualize the potential of AI to better exploit the massive volumes of environmental data from satellite and in situ sources that are critical for numerical weather prediction (NWP) and other Earth and environmental science applications. The main lessons communicated from community input via active workshop discussions and polling are reported. Finally, recommendations are presented for both scientists and decision-makers to address some of the challenges facing the adoption of AI across all Earth science.
Drought monitoring is important for characterizing the timing, extent, and severity of drought for effective mitigation and water management. Presented here is a novel satellite-based drought ...severity index (DSI) for regional monitoring derived using time-variable terrestrial water storage changes from the Gravity Recovery and Climate Experiment (GRACE). The GRACE-DSI enables drought feature comparison across regions and periods, it is unaffected by uncertainties associated with soil water balance models and meteorological forcing data, and it incorporates water storage changes from human impacts including groundwater withdrawals that modify land surface processes and impact water management. Here, the underlying algorithm is described, and the GRACE-DSI performance in the continental United States during 2002–14 is evaluated. It is found that the GRACE-DSI captures documented regional drought events and shows favorable spatial and temporal agreement with the monthly Palmer Drought Severity Index (PDSI) and the U.S. Drought Monitor (USDM). The GRACE-DSI also correlates well with a satellite-based normalized difference vegetation index (NDVI), indicating sensitivity to plant-available water supply changes affecting vegetation growth. Because the GRACE-DSI captures changes in total terrestrial water storage, it complements more traditional drought monitoring tools such as the PDSI by providing information about deeper water storage changes that affect soil moisture recharge and drought recovery. The GRACE-DSI shows potential for globally consistent and effective drought monitoring, particularly where sparse ground observations (especially precipitation) limit the use of traditional drought monitoring methods.
Here, we use 16‐year satellite and reanalysis data in combination with a multivariate regression model to investigate how aerosols affect cloud fraction (CF) over the East Coast of the United States. ...Cloud droplet number concentrations (Nd), cloud geometrical thickness, lower tropospheric stability, and relative humidity at 950 hPa (RH950) are identified as major cloud controlling parameters that explain 97% of the variability in CF. Nd is shown to play an important role in regulating the dependence of CF on RH950. The observed annual‐mean CF shows no significant trend due to the cancellation from the opposite trends in Nd and RH950. The multivariate regression model revealed that the decline in Nd alone would lead to about a 20% relative decline in CF. Our results indicate the significant aerosol effects on CF and suggest the need to account for pollution‐induced cloud changes in quantifying cloud feedback based on long‐term observations.
Plain Language Summary
Understanding how aerosols affect cloud cover is critical for reducing the large uncertainty of the radiative forcing associated with aerosol‐cloud interactions for future climate projections. By choosing a region (East Coast of the United States) that has experienced a significant decline in cloud droplet number concentrations due to pollution control in recent decades, we quantify aerosol effects on cloud amount based on long‐term satellite observation and reanalysis data in combination with a statistical regression model that isolates aerosol effects from meteorology influences. Our results reveal significant effects of aerosols on cloud amount and indicate the need to account for changes in cloud microphysics for constraining and quantifying cloud feedback based on long‐term observations.
Key Points
Nd plays an important role in regulating the dependence of cloud fraction (CF) on RH950
The observed annual‐mean CF shows no significant trend due to the cancellation from the opposite trends in Nd and RH950
When fixing the meteorology, we found significant aerosol effects on cloud amount based on long‐term satellite observations
In tropical forests, leaf phenology—particularly the pronounced dry-season green-up—strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the ...understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3 m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.
•A robust method cross-calibrated PlanetScope using BRDF-adjusted MODIS.•Calibrated data accurately assessed ecosystem-scale and crown-scale reflectance.•A dry-season decrease in non-photosynthetic vegetation (NPV) fraction was detected.•Large seasonal trend variability in crown-scale NPV fraction was quantified.
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