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Seung-Hee Ham, Seiji Kato, Fred G. Rose, Norman G. Loeb, Kuan-Man Xu, Tyler Thorsen, Michael G. Bosilovich, Sunny Sun-Mack, Yan Chen, and Walter F. Miller

Abstract

Cloud macrophysical changes over the Pacific Ocean from 2007 to 2017 are examined by combining CALIPSO and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño–Southern Oscillation (ENSO) events. For example, midcloud (cloud tops at 3–10 km) and high cloud (cloud tops at 10–18 km) amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, relative to CALCS, MODIS shows stronger anticorrelation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, relative to CALCS, MODIS shows smaller impacts of mid- and high clouds on the low troposphere (<3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid- and high clouds.

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Ronald D. Leeper, Bryan Petersen, Michael A. Palecki, and Howard Diamond

Abstract

Agricultural drought has traditionally been monitored using indices that are based on above-ground measures of temperature and precipitation that have lengthy historical records. However, the period-of-record length for soil moisture networks is becoming sufficient enough to standardize and evaluate soil moisture anomalies and percentiles that are spatially and temporally independent of local soil type, topography, and climatology. To explore these standardized measures in the context of drought, the U.S. Climate Reference Network hourly standardized soil moisture anomalies and percentiles were evaluated against changes in the U.S. Drought Monitor (USDM) status, with a focus on onset, worsening, and improving drought conditions. The purpose of this study was to explore time scales (i.e., 1–6 weeks) and soil moisture at individual (i.e., 5, 10, 20, 50, and 100 cm) and aggregated layer (i.e., top and column) depths to determine those that were more closely align with evolving drought conditions. Results indicated that the upper-level depths (5, 10, and 20 cm, and top layer aggregate) and shorter averaging periods were more responsive to changes in USDM drought status. This was particularly evident during the initial and latter stages of drought when USDM status changes were thought to be more aligned with soil moisture conditions. This result indicates that standardized measures of soil moisture can be useful in drought monitoring and forecasting applications during these critical stages of drought formation and amelioration.

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Julia A. Shates, Claire Pettersen, Tristan S. L’Ecuyer, Steven J. Cooper, Mark S. Kulie, and Norman B. Wood

Abstract

The prevailing snowfall regimes at two Scandinavian sites, Haukeliseter, Norway, and Kiruna, Sweden, are documented using ground-based in situ and remote sensing methods. Micro Rain Radar (MRR) profiles indicate three distinct snowfall regimes occur at both sites: shallow, deep, and intermittent snowfall. The shallow snowfall regime produces the lowest mean snowfall rates and radar echo tops are confined below 1.5 km above ground level (AGL). Shallow snowfall occurs under areas of large-scale subsidence with a moist boundary layer and dry air aloft. The atmospheric ridge coinciding with shallow snowfall is highly anomalous over Haukeliseter but is more common in Kiruna where shallow snowfall was frequently observed. The shallow snowfall particle size distributions (PSDs) are broad with lower particle concentrations than other regimes, especially small particles. Deep snowfall events exhibit MRR profiles that extend above 2 km AGL and tend to be associated with weak low pressure and high relative humidity throughout the troposphere. The PSDs in deep events are narrower with high concentrations of small particles. Increasing MRR reflectivity toward the surface suggests aggregation as a possible growth process during deep snowfall events. The heaviest mean snowfall rates are associated with intermittent events that are characterized by deep MRR profiles but have variations in intensity and height. The intermittent regime is associated with anomalous, deep low pressure along the coast of Norway and enhanced relative humidity at lower levels. The PSDs reveal high concentrations of small and large particles. The analysis reveals that there are unique characteristics of shallow, deep, and intermittent snowfall regimes that are common between the sites.

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Dominique Bouniol, Rémy Roca, Thomas Fiolleau, and Patrick Raberanto

Abstract

The evolution of radiative properties [outgoing longwave radiation (OLR) and albedo at the top of the atmosphere] over a mesoscale convective system (MCS) life cycle is assessed using five years of Scanner for Radiation Budget (ScaRaB) radiometer on board the Megha-Tropiques satellite merged with geostationary infrared images. The MCS life cycle is documented using a tracking algorithm. A composite approach is then implemented to document the evolution of radiative properties at each life stage at the scale of the tropical belt, in continental and oceanic regions and in specific regions. Independently of the considered region, the composites share similarities with a unique maximum in albedo and a unique minimum in OLR, values of which differ depending on the environment as well as the amplitude of both parameters over the life cycle. The unique precessing orbit of the Megha-Tropiques satellite allows a consideration of the albedo as a function of the local time of observation showing that the magnitude of the albedo signal is mainly controlled by the solar zenithal angle. Sensitivity tests make possible the quantification of the impact of an error in radiative properties showing that even small errors lead to substantial increment on the instantaneous cloud radiative effect. All together, these elements point toward the subtle balance between life cycle, cloud radiative properties, and phasing within the diurnal cycle to build the atmospheric radiative budget in oceanic or continental regions.

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Bianca Adler, James M. Wilczak, Laura Bianco, Irina Djalalova, James B. Duncan Jr., and David D. Turner

Abstract

Persistent cold pools form as layers of cold stagnant air within topographical depressions mainly during wintertime, when the near-surface air cools and/or the air aloft warms and daytime surface heating is insufficient to mix out the stable layer. An area often affected by persistent cold pools is the Columbia River basin in the Pacific Northwest, when a high pressure system east of the Cascade Range promotes radiative cooling and easterly flow. The only major outflow for the easterly flow is through the narrow Columbia River Gorge that cuts through the north–south-oriented Cascade Range and often experiences very strong gap flows. Observations collected during the Second Wind Forecast Improvement Project (WFIP2) are used to study a persistent cold pool in the Columbia River basin between 10 and 19 January 2017 that was associated with a strong gap flow. We used data from various remote sensing and in situ instruments and an optimal estimation physical retrieval to obtain thermodynamic profiles to address the temporal and spatial characteristics of the cold pool and gap flow and to investigate the physical processes involved during formation, maintenance, and decay. While large-scale temperature advection occurred during all phases, we found that the cold-pool vertical structure was modulated by the existence of low-level clouds and that turbulent shear-induced mixing and downslope wind storms likely played a role during its decay.

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Jacob T. Carlin, Heather D. Reeves, and Alexander V. Ryzhkov

Abstract

Snow sublimating in dry air is a forecasting challenge and can delay the onset of surface snowfall and affect storm-total accumulations. Despite this fact, it remains comparatively less studied than other microphysical processes. Herein, the characteristics of sublimating snow and the potential for nowcasting snowfall reaching the surface are explored through the use of dual-polarization radar. Twelve cases featuring prolific sublimation were analyzed using range-defined quasi-vertical profiles (RDQVPs) and were compared with environmental model analyses. Overall, reflectivity Z significantly decreases, differential reflectivity Z DR slightly decreases, and copolar-correlation coefficient ρ hv remains nearly constant through the sublimation layer. Regions of enhanced specific differential phase K dp were frequently observed in the sublimation layer and are believed to be polarimetric evidence of secondary ice production via sublimation. A 1D bin model was initialized using particle size distributions retrieved from the RDQVPs using numerous novel polarimetric snow retrieval relations for a wide range of forecast lead times, with the model environment evolving in response to sublimation. It was found that the model was largely able to predict the snowfall start time up to 6 h in advance, with a 6-h median bias of just −18.5 min. A more detailed case study of the 8 December 2013 snowstorm in the Philadelphia, Pennsylvania, region was also performed, demonstrating good correspondence with observations and examples of model fields (e.g., cooling rate) hypothetically available from such a tool. The proof-of-concept results herein demonstrate the potential benefits of incorporating spatially averaged radar data in conjunction with simple 1D models into the nowcasting process.

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Baojuan Huai, Michiel R. van den Broeke, Carleen H. Reijmer, and John Cappellen

Abstract

This paper estimates rainfall totals at 17 Greenland meteorological stations, subjecting data from in situ precipitation gauge measurements to seven different precipitation phase schemes to separate rainfall and snowfall amounts. To correct the resulting snow/rain fractions for undercatch, we subsequently use a dynamic correction model (DCM) for automatic weather stations (AWS, Pluvio gauges) and a regression analysis correction method for staffed stations (Hellmann gauges). With observations ranging from 5% to 57% for cumulative totals, rainfall accounts for a considerable fraction of total annual precipitation over Greenland’s coastal regions, with the highest rain fraction in the south (Narsarsuaq). Monthly precipitation and rainfall totals are used to evaluate the regional climate model RACMO2.3. The model realistically captures monthly rainfall and total precipitation (R = 0.3–0.9), with generally higher correlations for rainfall for which the undercatch correction factors (1.02–1.40) are smaller than those for snowfall (1.27–2.80), and hence the observations are more robust. With a horizontal resolution of 5.5 km and simulation period from 1958 to the present, RACMO2.3 therefore is a useful tool to study spatial and temporal variability of rainfall in Greenland, although further statistical downscaling may be required to resolve the steep rainfall gradients.

Open access
Christoph Böhm, Jan H. Schween, Mark Reyers, Benedikt Maier, Ulrich Löhnert, and Susanne Crewell

Abstract

In many hyperarid ecosystems, such as the Atacama Desert, fog is the most important freshwater source. To study biological and geological processes in such water-limited regions, knowledge about the spatiotemporal distribution and variability of fog presence is necessary. In this study, in situ measurements provided by a network of climate stations equipped, inter alia, with leaf wetness sensors are utilized to create a reference fog dataset that enables the validation of satellite-based fog retrieval methods. Further, a new satellite-based fog-detection approach is introduced that uses brightness temperatures measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) as input for a neural network. Such a machine learning technique can exploit all spectral information of the satellite data and represent potential nonlinear relationships. Relative to a second fog-detection approach based on MODIS cloud-top height retrievals, the neural network reaches a higher detection skill (Heidke skill score of 0.56 as compared with 0.49). A suitable representation of temporal variability on subseasonal time scales is provided with correlations mostly greater than 0.7 between fog occurrence time series derived from the neural network and the reference data for individual climate stations, respectively. Furthermore, a suitable spatial representativity of the neural-network approach to expand the application to the whole region is indicated. Three-year averages of fog frequencies reveal similar spatial patterns for the austral winter season for both approaches. However, differences are found for the summer and potential reasons are discussed.

Open access
Nayeong Cho, Jackson Tan, and Lazaros Oreopoulos

Abstract

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 prespecified 3-h UTC intervals that spatiotemporally match International Satellite Cloud Climatology Project (ISCCP) gridded cloud data. The other edition comes from the 1° 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.

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