We present and evaluate a climatology of cloud droplet number concentration (CDNC) based on 13 years of Aqua-MODIS observations. The climatology provides monthly mean 1 × 1° CDNC values plus ...associated uncertainties over the global ice-free oceans. All values are in-cloud values, i.e. the reported CDNC value will be valid for the cloudy part of the grid box. Here, we provide an overview of how the climatology was generated and assess and quantify potential systematic error sources including effects of broken clouds, and remaining artefacts caused by the retrieval process or related to observation geometry. Retrievals and evaluations were performed at the scale of initial MODIS observations (in contrast to some earlier climatologies, which were created based on already gridded data). This allowed us to implement additional screening criteria, so that observations inconsistent with key assumptions made in the CDNC retrieval could be rejected. Application of these additional screening criteria led to significant changes in the annual cycle of CDNC in terms of both its phase and magnitude. After an optimal screening was established a final CDNC climatology was generated. Resulting CDNC uncertainties are reported as monthly-mean standard deviations of CDNC over each 1 × 1° grid box. These uncertainties are of the order of 30 % in the stratocumulus regions and 60 to 80 % elsewhere.
This paper proposes a novel approach for hydrometeor classification using passive microwave observations. The use of passive measurements for this purpose has not been extensively explored, despite ...being available for over four decades. We utilize the Micro-Wave Humidity Sounder-2 (MWHS-2) to relate microwave brightness temperatures to hydrometeor types derived from the global precipitation measurement’s (GPM) dual-frequency precipitation radar (DPR), which are classified into liquid, mixed, and ice phases. To achieve this, we utilize a convolutional neural network model with an attention mechanism that learns feature representations of MWHS-2 observations from spatial and temporal dimensions. The proposed algorithm classified hydrometeors with 84.7% accuracy using testing data and captured the geographical characteristics of hydrometeor types well in most areas, especially for frozen precipitation. We then evaluated our results by comparing predictions from a different year against DPR retrievals seasonally and globally. Our global annual cycles of precipitation occurrences largely agreed with DPR retrievals with biases being 8.4%, −11.8%, and 3.4%, respectively. Our approach provides a promising direction for utilizing passive microwave observations and deep-learning techniques in hydrometeor classification, with potential applications in the time-resolved observations of precipitation structure and storm intensity with a constellation of smallsats (TROPICS) algorithm development.
This paper describes three algorithms for retrieving precipitation over oceans from brightness temperatures (TBs) of the Micro-Wave Humidity Sounder-2 (MHWS-2) onboard Fengyun-3C (FY-3C). For ...algorithm development, scattering-induced TB depressions (ΔTBs) of MWHS-2 at channels between 89 and 190 GHz were collocated to rain rates derived from measurements of the Global Precipitation Measurement’s Dual-frequency Precipitation Radar (DPR) for the year 2017. ΔTBs were calculated by subtracting simulated cloud-free TBs from bias-corrected observed TBs for each channel. These ΔTBs were then related to rain rates from DPR using (1) multilinear regression (MLR); the other two algorithms, (2) range searches (RS) and (3) nearest neighbor searches (NNS), are based on k-dimensional trees. While all three algorithms produce instantaneous rain rates, the RS algorithm also provides the probability of precipitation and can be understood in a Bayesian framework. Different combinations of MWHS-2 channels were evaluated using MLR and results suggest that adding 118 GHz improves retrieval performance. The optimal combination of channels excludes high-peaking channels but includes 118 GHz channels peaking in the mid and high troposphere. MWHS-2 observations from another year were used for validation purposes. The annual mean 2.5° × 2.5° gridded rain rates from the three algorithms are consistent with those from the Global Precipitation Climatology Project (GPCP) and DPR. Their correlation coefficients with GPCP are 0.96 and their biases are less than 5%. The correlation coefficients with DPR are slightly lower and the maximum bias is ∼8%, partly due to the lower sampling density of DPR compared to that of MWHS-2.
The Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP), an updated and enhanced version of the University of Wisconsin (UWisc) cloud liquid water path (CLWP) climatology, currently ...provides 29 years (1988–2016) of monthly gridded (1°) oceanic CLWP information constructed using Remote Sensing Systems (RSS) intercalibrated 0.25°-resolution retrievals. Satellite sources include SSM/I, TMI, AMSR-E, WindSat, SSMIS, AMSR-2, and GMI. To mitigate spurious CLWP trends, the climatology is corrected for drifting satellite overpass times by simultaneously solving for the monthly average CLWP and the monthly mean diurnal cycle. In addition to a longer record and six additional satellite products, major enhancements relative to the UWisc climatology include updating the input to version 7 RSS retrievals, correcting for a CLWP bias (based on matchups to clear-sky MODIS scenes), and constructing a total (cloud + rain) liquid water path (TLWP) record for use in analyses of columnar liquid water in raining clouds. Because the microwave emission signal from cloud water is similar to that of precipitation-sized hydrometeors, greater uncertainty in the CLWP record is expected in regions of substantial precipitation. Therefore, the TLWP field can also be used as a quality-control screen,where uncertainty increases as the ratio of CLWP to TLWP decreases. For regions where confidence in CLWP is highest (i.e., CLWP:TLWP > 0.8), systematic differences in MAC CLWP relative to UWisc CLWP range from −15% (e.g., global oceanic stratocumulus decks) to +5%–10% (e.g., portions of the higher latitudes, storm tracks, and shallower convection regions straddling the ITCZ). The dataset is currently hosted at the Goddard Earth Sciences Data and Information Services Center.
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BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Long‐term observational data reveal that both the frequency and amount of light rain have decreased in eastern China (EC) for 1956–2005 with high spatial coherency. This is different from the trend ...of total rainfall observed in EC, which decreases in northern EC and increases in southern EC. To examine the cause of the light rain trends, we analyzed the long‐term variability of atmospheric water vapor and its correlation with light rain events. Results show very weak relationships between large‐scale moisture transport and light rain in EC. Because of human activities, pollutant emission has increased dramatically in China for the last few decades, leading to a significant reduction in visibility between 1960 and 2000. Cloud‐resolving model simulations over EC show that aerosols corresponding to polluted conditions can significantly increase the cloud droplet number concentration (CDNC) and reduce droplet sizes compared to pristine conditions. This can lead to a significant decline in raindrop concentration and delay raindrop formation because smaller cloud droplets are less efficient in the collision and coalescence processes. Together with weaker convection, the precipitation frequency and amount are significantly reduced in the polluted case in EC. Satellite data also reveal higher CDNC and smaller droplet size over polluted land in EC relative to pristine regions, which is consistent with the model results. Observational evidences and simulations results suggest that the significantly increased aerosol concentrations produced by air pollution are at least partly responsible for the decreased light rain events observed in China over the past 50 years.
The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall ...rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall events in the CloudSat snowfall dataset. The remaining 5% of snowfall events are distributed between other categories. Distinct oceanic versus continental trends exist between the two major snowfall categories, as shallow cumuliform snow-producing clouds occur predominantly over the oceans. Regional differences are also noted in the partitioned dataset, with over-ocean regions near Greenland, the far North Atlantic Ocean, the Barents Sea, the western Pacific Ocean, the southern Bering Sea, and the Southern Hemispheric pan-oceanic region containing distinct shallow snowfall occurrence maxima exceeding 60%. Certain Northern Hemispheric continental regions also experience frequent shallow cumuliform snowfall events (e.g., inland Russia), as well as some mountainous regions. CloudSat-generated snowfall rates are also partitioned between the two major snowfall categories to illustrate the importance of shallow snow-producing cloud structures to the average annual snowfall. While shallow cumuliform snowfall produces over 50% of the annual estimated surface snowfall flux regionally, about 18% (82%) of global snowfall is attributed to shallow (nimbostratus) snowfall. This foundational spaceborne snowfall study will be utilized for follow-on evaluative studies with independent model, reanalysis, and ground-based observational datasets to characterize respective dataset biases and to better quantify CloudSat snowfall detection and quantitative snowfall estimate uncertainties.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This work describes a new climatology of cloud liquid water path (LWP), termed the University of Wisconsin (UWisc) climatology, derived from 18 yr of satellite-based passive microwave observations ...over the global oceans. The climatology is based on a modern retrieval methodology applied consistently to the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS) (AMSR-E) microwave sensors on eight different satellite platforms, beginning in 1988 and continuing through 2005. It goes beyond previously published climatologies by explicitly solving for the diurnal cycle of cloud liquid water by providing statistical error estimates, and includes a detailed discussion of possible systematic errors.
A novel methodology for constructing the climatology is used in which a mean monthly diurnal cycle as well as monthly means of the liquid water path are derived simultaneously from the data on a 1° grid; the methodology also produces statistical errors for these quantities, which decrease toward the end of the time record as the number of observations increases. The derived diurnal cycles are consistent with previous findings in the tropics, but are also derived for higher latitudes and contain more information than in previous studies. The new climatology exhibits differences with previous observationally based climatologies and is found to be more consistent with the 40-yr ECMWF Re-Analysis (ERA-40) than are the previous climatologies.
Potential systematic errors of the order of 15%–30% or higher exist in the LWP climatology. A previously unexplored source of systematic error is caused by the assumption that all microwave-based retrievals of LWP must make regarding the partitioning of cloud water and rainwater, which cannot be determined using microwave observations alone. The potentially large systematic errors that result may hamper the usefulness of microwave-based climatologies of both cloud liquid water and especially rain rate, particularly in certain regions of the tropics and midlatitudes where the separation of rain from liquid cloud water is most critical.
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Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Volcanoes that deposit eruptive products into the ocean can trigger phytoplankton blooms near the deposition area. Phytoplankton blooms impact the global carbon cycle, but the specific conditions and ...mechanisms that facilitate volcanically triggered blooms are not well understood, especially in low nutrient ocean regions. We use satellite remote sensing to analyze the chlorophyll response to an 8‐month period of explosive and effusive activity from Nishinoshima volcano, Japan. Nishinoshima is an ocean island volcano in a low nutrient low chlorophyll region of the Northern Pacific Ocean. From June to August 2020, during explosive activity, satellite‐derived chlorophyll‐a was detectable with amplitudes significantly above the long‐term climatological value. After the explosive activity ceased in mid‐August 2020, these areas of heightened chlorophyll concentration decreased as well. In addition, we used aerial observations and satellite imagery to demonstrate a spatial correlation between blooms and ash plume direction. Using a sun‐induced chlorophyll‐a fluorescence satellite product, we confirmed that the observed chlorophyll blooms are phytoplankton blooms. Based on an understanding of the nutrients needed to supply blooms, we hypothesize that blooms of nitrogen‐fixing phytoplankton led to a 1010–1012 g drawdown of carbon. Thus, the bloom could have significantly mediated the output of carbon from the explosive phase of the eruption but is a small fraction of anthropogenic CO2 stored in the ocean or the global biological pump. Overall, we provide a case study of fertilization of a nutrient‐poor ocean with volcanic ash and demonstrate a scenario where multi‐month scale deposition triggers continuous phytoplankton blooms across 1,000s of km2.
Plain Language Summary
Volcanic eruptions can cause organisms known as phytoplankton to multiply and form what is known as a phytoplankton bloom in the ocean. Phytoplankton blooms can impact the life cycle of carbon in the earth system, but it is not always obvious why phytoplankton blooms happen. Using different satellite data, we observe phytoplankton blooms by viewing chlorophyll concentration in the ocean. Nishinoshima is a remote volcano in an area of the Pacific that lacks nutrients necessary for phytoplankton blooms. Nishinoshima erupted in 2019–2020 and deposited lava and ash into the ocean at different times. By looking at the chlorophyll concentration during the time periods lava and ash were deposited into the ocean, we found that chlorophyll concentration increased when ash was deposited into the ocean. These increases in chlorophyll concentration were determined to be phytoplankton blooms. These phytoplankton blooms may utilize nutrients from volcanic ash and the atmosphere, leading to a drawdown of atmospheric carbon.
Key Points
Ash deposition triggers phytoplankton blooms at Nishinoshima during the explosive phase of the 2019–2020 eruption
Phytoplankton blooms were not present during the effusive phase of the 2019–2020 eruption
Phytoplankton blooms triggered by ash deposition can lead to carbon drawdown that can mediate the carbon output from the eruption
Abstract
The 15 January 2022 eruption of Hunga Tonga-Hunga Ha’apai, and the preceding eruptions on 19 December 2021 and 13 January 2022, were remarkable, partly because the eruptions generated ...extensive umbrella clouds, regions where the volcanic clouds spread laterally. Here we use satellite remote sensing to evaluate the umbrella cloud tops’ heights, longevities, water contents, and volumetric flow rates. We identified two umbrella clouds at distinct elevations on 15 January 2022. Specifically, after 05:30 UTC, the strong westward propagation of an upper umbrella cloud at 31 km ± 3 km enabled the visibility of the lower umbrella cloud at 17 km ± 2 km. The satellite-derived volumetric flow rate for 15 January 2022 was ~5.0 × 10
11
m
3
s
−1
, nearly two orders of magnitude higher than the volumetric flow rates estimated for the 19 December 2021 and 13 January 2022 eruptions. Finally, we found that the umbrellas on all three dates were ice-rich.
The GEWEX Water Vapor Assessment Schröder, Marc; Lockhoff, Maarit; Forsythe, John M. ...
Journal of applied meteorology and climatology,
07/2016, Letnik:
55, Številka:
7
Journal Article
Recenzirano
Odprti dostop
The Global Energy and Water Cycle Exchanges project (GEWEX) water vapor assessment’s (G-VAP) main objective is to analyze and explain strengths and weaknesses of satellite-based data records of water ...vapor through intercomparisons and comparisons with ground-based data. G-VAP results from the intercomparison of six total column water vapor (TCWV) data records are presented. Prior to the intercomparison, the data records were regridded to a common regular grid of 2° × 2° longitude–latitude. All data records cover a common period from 1988 to 2008. The intercomparison is complemented by an analysis of trend estimates, which was applied as a tool to identify issues in the data records. It was observed that the trends over global ice-free oceans are generally different among the different data records. Most of these differences are statistically significant. Distinct spatial features are evident in maps of differences in trend estimates, which largely coincide with maxima in standard deviations from the ensemble mean. The penalized maximal F test has been applied to global ice-free ocean and selected land regional anomaly time series, revealing differences in trends to be largely caused by breakpoints in the different data records. The time, magnitude, and number of breakpoints typically differ from region to region and between data records. These breakpoints often coincide with changes in observing systems used for the different data records. The TCWV data records have also been compared with data from a radiosonde archive. For example, at Lindenberg, Germany, and at Yichang, China, such breakpoints are not observed, providing further evidence for the regional imprint of changes in the observing system.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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