Evaluation of GPROF V05 Precipitation Retrievals under Different Cloud Regimes

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard Profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

Limitations of bin and bulk microphysics in reproducing the observed spatial structure of light precipitation

Coarse-gridded atmospheric models often account for subgrid-scale variability by specifying probability distribution functions (PDFs) of process rate inputs such as cloud and rain water mixing ratios (qc and qr, respectively). PDF parameters can be obtained from numerous sources: in situ observations, ground- or space-based remote sensing, or fine-scale modeling such as large eddy simulation (LES). LES is appealing to constrain PDFs because it generates large sample sizes, can simulate a variety of cloud regimes/case studies, and is not subject to the ambiguities of observations. However, despite the appeal of using model output for parameterization development, it has not been demonstrated that LES satisfactorily reproduces the observed spatial structure of microphysical fields. In this study, the structure of observed and modeled microphysical fields are compared by applying bifractal analysis, an approach that quantifies variability across spatial scales, to simulations of a drizzling stratocumulus field that span a range of domain sizes, drop concentrations (a proxy for mesoscale organization), and microphysics schemes (bulk and bin). Simulated qc closely matches observed estimates of bifractal parameters that measure smoothness and intermittency. There are major discrepancies between observed and simulated qr properties, though, with bulk simulated qr consistently displaying the bifractal properties of observed clouds (smooth, minimally intermittent) rather than rain while bin simulations produce qr that is appropriately intermittent but too smooth. These results suggest fundamental limitations of bulk and bin schemes to realistically represent higher-order statistics of the observed rain structure.

Controls on surface aerosol particle number concentrations and aerosol-limited cloud regimes over the central Greenland Ice Sheet

This study presents the first full annual cycle (2019–2020) of ambient surface aerosol particle number concentration measurements (condensation nuclei > 20 nm, N20) collected at Summit Station (Summit), in the centre of the Greenland Ice Sheet (72.58∘ N, −38.45∘ E; 3250 ma.s.l.). The mean surface concentration in 2019 was 129 cm−3, with the 6 h mean ranging between 1 and 1441 cm−3. The highest monthly mean concentrations occurred during the late spring and summer, with the minimum concentrations occurring in February (mean: 18 cm−3). High-N20 events are linked to anomalous anticyclonic circulation over Greenland and the descent of free-tropospheric aerosol down to the surface, whereas low-N20 events are linked to anomalous cyclonic circulation over south-east Greenland that drives upslope flow and enhances precipitation en route to Summit. Fog strongly affects particle number concentrations, on average reducing N20 by 20 % during the first 3 h of fog formation. Extremely-low-N20 events (< 10 cm−3) occur in all seasons, and we suggest that fog, and potentially cloud formation, can be limited by low aerosol particle concentrations over central Greenland.

Spaceborne differential absorption radar water vapor retrieval capabilities in tropical and subtropical boundary layer cloud regimes

Differential absorption radar (DAR) near the 183 GHz water vapor absorption line is an emerging measurement technique for humidity profiling inside of clouds and precipitation with high vertical resolution, as well as for measuring integrated water vapor (IWV) in clear-air regions. For radar transmit frequencies on the water line flank away from the highly attenuating line center, the DAR system becomes most sensitive to water vapor in the planetary boundary layer (PBL), which is a region of the atmosphere that is poorly resolved in the vertical by existing spaceborne humidity and temperature profiling instruments. In this work, we present a high-fidelity, end-to-end simulation framework for notional spaceborne DAR instruments that feature realistically achievable radar performance metrics and apply this simulator to assess DAR's PBL humidity observation capabilities. Both the assumed instrument parameters and radar retrieval algorithm leverage recent technology and algorithm development for an existing airborne DAR instrument. To showcase the capabilities of DAR for humidity observations in a variety of relevant PBL settings, we implement the instrument simulator in the context of large eddy simulations (LESs) of five different cloud regimes throughout the trade-wind subtropical-to-tropical cloud transition. Three distinct DAR humidity observations are investigated: IWV between the top of the atmosphere and the first detected cloud bin or Earth's surface; in-cloud water vapor profiles with 200 meter vertical resolution; and IWV between the last detected cloud bin and the Earth's surface, which can provide a precise measurement of the sub-cloud humidity. We provide a thorough assessment of the systematic and random errors for all three measurement products for each LES case and analyze the humidity precision scaling with along-track measurement integration. While retrieval performance depends greatly on the specific cloud regime, we find generally that for a radar with cross-track scanning capability, in-cloud profiles with 200 m vertical resolution and 10 %–20 % uncertainty can be retrieved for horizontal integration distances of 100–200 km. Furthermore, column IWV can be retrieved with 10 % uncertainty for 10–20 km of horizontal integration. Finally, we provide some example science applications of the simulated DAR observations, including estimating near-surface relative humidity using the cloud-to-surface column IWV and inferring in-cloud temperature profiles from the DAR water vapor profiles by assuming a fully saturated environment.

Classifying planetary cloudiness with an updated set of MODIS Cloud Regimes

We present an updated Cloud Regime (CR) dataset based on Moderate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 cloud products, specifically joint histograms that partition cloud fraction within distinct combinations of cloud top pressure and cloud optical thickness ranges. The paper focuses on an edition of the CR dataset derived from our own aggregation of MODIS pixel-level cloud retrievals on an equal area grid and pre-specified 3-hour UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1-degree daily aggregation provided by standard MODIS Level-3 data, as in previous versions of the MODIS CRs, for easier use with datasets mapped on equal angle grids. Both editions consist of 11 clusters whose centroids are nearly identical.We provide a physical interpretation of the new CRs and aspects of their climatology that have not been previously examined, such as seasonal and interannual variability of CR frequency of occurrence. We also examine the makeup and precipitation properties of the CRs assisted by independent datasets originating from active observations, and provide a first glimpse of how MODIS CRs relate to clouds as seen by ISCCP.

Contrasting Cloud Regimes and Associated Rainfall over the South Asian and East Asian Monsoon Regions

The Asian monsoon has large spatial and temporal variabilities in winds and precipitation. This study reveals that the Asian monsoon also exhibits pronounced regional differences in cloud regimes and cloud–rainfall relationship at a wide range of time scales from diurnal to seasonal to interannual. Over South (East) Asia, the convectively active regime of deep convection (CD) occurs frequently in June–September (March–September) with a late-afternoon peak (morning feature). The intermediate mixture (IM) regime over South Asia mainly occurs in summer and maximizes near noon. It develops as CD at late afternoon and dissipates as convective cirrus (CC) after midnight, showing a life cycle of thermal convection in response to solar radiation. Over East Asia, IM is dominant in cold seasons with a small diurnal cycle, indicating a prevalence of midlevel stratiform clouds. Further analyses show that CD and CC contribute 80%–90% of the rainfall amount and most of the intense rainfall in the two key regions. The CD-related rainfall also accounts for the pronounced diurnal cycles of summer rainfall with a late-afternoon peak (morning feature) over northern India (Southeast China). The afternoon CD-related rainfall mainly results from thermal convection under the moderate humidity but warm conditions particularly over northern India, while the morning CD-related rainfall over Southeast China is more related to the processes with high humidity. The CD/CC-related rainfall also exhibits large interannual variations that explain ~90% of the interannual variance of summer rainfall. The interannual variations of CD/CC occurrence are positively correlated with the moist southerlies and induced convergence, especially over Southeast China, suggesting a close relationship between cloud regimes and monsoon activities.

Cloud regime analysis over Central Europe based on 14 years of satellite data

&lt;p&gt;&lt;span&gt;The improvement of our understanding of the spatiotemporal variability of cloud properties and their governing processes is of high importance, given the crucial role of clouds in the climate system. The availability of long-term and high-quality satellite observations together with mature remote sensing techniques has made feasible the creation of multi-decadal climate data records for this purpose.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Various cloud classification techniques have been developed and applied in the past, each with distinct advantages and disadvantages, allowing studying clouds from different perspectives. One of these techniques is the creation of cloud regimes which provides information on the prevalence of simultaneously occurring cloud types over a region. This study uses the k-means clustering method, applied to 2-dimensional histograms of cloud top pressure and optical thickness, in order to derive and analyze cloud regimes over Europe during the last decade. Europe is selected for this work because it is an appropriate region for studying cloud regimes since the prevailing atmospheric circulation patterns and its diverse geomorphology, result in a mixture of diverse cloud types. In order to achieve that, the CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), is used as basis for the derivation of the cloud regimes. In particular, pixel-level Cloud Optical Thickness (COT) and Cloud Top Pressure (CTP) products of CLAAS-2.1, from 2004 to 2017, are used in order to compute 2D histograms on a 1&amp;#176;&amp;#215;1&amp;#176; spatial resolution. Then the k-means clustering algorithm is applied, treating each 2D COT-CTP histogram of each grid point and time step as an individual data point. Various sensitivity studies on the subsampling of the data and the selection of the cloud regimes were carried out, in order to test the robustness of the method and of the results.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In contrast to the previous studies and taking advantage of the geostationary orbit of Meteosat Second Generation (MSG), on which SEVIRI is aboard, a better sampling of the diurnal cycle of clouds is thus included in the derivation process of cloud regimes. Furthermore, the annual cycle of the produced cloud regimes is examined. In addition, for each regime, the time step with its highest spatial frequency of occurrence is selected for a visual comparison with the corresponding RGB image. Finally, a comparison of the cloud regimes against the synoptic large scale weather pattern classification is investigated. The weather pattern classification consists of 29 typical defined patterns of the daily synoptic circulation and it is produced by the German Weather Service (DWD).&lt;/span&gt;&lt;/p&gt;