Abstract. Regional heavy pollution events in East China (110–122° E, 28–40° N) are the main environmental problems recently because of the high urbanization and rapid economic development connected with too much emissions of pollutants. However, appropriate weather condition is another factor which cannot be ignored for these events. In this study, the relationship between regional pollution status and larger scale atmospheric circulations over East China in October is investigated using ten-year (2001–2010) MODIS/Terra aerosol optical depth (AOD) product and the NCEP reanalysis data together with case analysis and composite analysis. Generally, statistics in East China show values of mean AOD vary from 0.3 to 0.9 in October over the region, and larger AOD variances are accompanied with the distribution of higher average AOD. Eighteen pollution episodes (regional mean AOD > 0.6) and ten clean episodes (regional mean AOD < 0.4) are selected and then categorized into six polluted types and three clean types, respectively. Each type represents different weather pattern associated with the combination of lower and upper atmospheric circulation. Generally, the uniform surface pressure field in East China or steady straight westerly in middle troposphere, particularly the rear of anticyclone at 850 hPa, are typical weather patterns responsible for heavy pollution events, while clean episodes occur when strong southeastward cold air advection prevails below the middle troposphere or air masses are transported from sea to the mainland. The above studies are especially useful to the government decision make on balance of economic activities and pollution mitigations.

A novel method has been proposed for validating satellite radar snowfall retrievals using surface station observations over the western United States mountainous region, where the mean snowfall rate at a station depends on its elevation. First, all station data within a 1° × 1° grid are used to develop a snowfall rate versus elevation relation. This relation is then used to compute snowfall rate in other locations within the 1° × 1° grid, as if surface observations were available everywhere in the grid. Grid mean snowfall rates are then derived, which should be more representative to the mean snowfall rate of the grid than using data at any one station or from a simple mean of all stations in the grid. Comparison of the so-derived grid mean snowfall rates with CloudSat retrievals shows that the CloudSat product underestimates snowfall by about 65% when averaged over all the 768 grids in the western United States mountainous regions. The bias does not seem to have clear dependency on elevation but strongly depends on snowfall rate. As an application of the method, we further estimated the snowfall to precipitation ratio using both ground and satellite measured data. It is found that the rates of increase with elevation of the snowfall to precipitation ratio are quite similar when calculating from ground and satellite data, being about 25% per kilometer elevation up or approximately 4% per every degree Cuisses of temperature drop.

&lt;p&gt;The central theme of this study is to explore if and how the intensity of a tropical cyclone (TC) is related to its size. This subject has puzzled atmospheric scientists since the work of Depperman (1947) but the existence of this relationship still remains elusive. The improved understanding of the intensity-size relationship of TCs will help coastal communities to prepare for the maximum potential damage as both the intensity and size have important impacts on wind damages, storm surges, and flooding. This study considers 33 years (1988&amp;#8211;2020) of TC records of maximum surface winds and radii of maximum and gale-force winds over the North Atlantic Basin derived from the Extended Best Track Dataset. Analysis of these TC records reveals a robust positive correlation between loss of earth and relative angular momentum. This finding together with the inspiration from the seminal work of Emanuel and his collaborators leads us to combine absolute angular momentum and its frictional loss as a radially invariant quantity, referred to as &amp;#8220;effective absolute angular momentum&amp;#8221; (eAAM), for radial profiles of TC surface winds. It is demonstrated that the eAAM model can reproduce the observed complex intensity-size relationship of TCs, which can be further reduced to a quasi-linear one after factoring out the angular momentum loss and the radius of maximum surface winds. The findings of this study would not only advance our understanding of the complex TC intensity-size relation, but also allow for operational assessments of TC severity and potential damage just using its outer wind information.&lt;/p&gt;

Abstract. Ground-based radar and radiometer data observed during the 2017–18 winter were used to simultaneously estimate both cloud liquid water path and snowfall rate for three types of snowing clouds: near-surface, shallow and deep. Surveying all the observed data, it is found that near-surface cloud is the most frequently observed cloud type with an area fraction of over 60 %, while deep cloud contributes the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with vast majority hardly reaching to 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, liquid water path in the three types of clouds all has substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as a priori database. Under idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.33, 0.48 and 0.74, respectively, for near-surface, shallow and deep snowing clouds over land surface; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increased from 0.33 to 0.54, while virtually no change in skills is found for deep clouds and only marginal improvement is found for shallow clouds. This study provides a general picture of the microphysical characteristics of the different types of snowing clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.