Raindrop Size Distributions (RSDs) samples from 15 flight missions though 6 hurricanes collected by Precipitation Imaging Probe (PIP) during National Oceanic and Atmospheric Administration’s hurricane field program in 2020 are used to study gamma fits of the RSDs in hurricanes. The method of moment (MM) is adopted for solving for the three parameters in gamma distribution. The results show that the usage of lower (higher) moments produces large biases for integral rain variables (IRV) of higher (lower) moments. These biases can be alleviated by extracting the best fits from five groups that use increasing higher orders of moments for MM. An intercept (N0)— slope (λ) relation identified from the fitted gamma distributions captures 92% of the variance of the data, where the majority of remaining 8% can be further captured by including the impact of liquid water content (LWC), as shown in the results from a random forest regression model.

Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration’s hurricane field program in 2020 are used to study reflectivity ( Z ) and rainfall rate (RR) ( R ) relationship (i.e., Z ‐ R relationship). The results show that the Z ‐ R distribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr −1 , after which it decreases rapidly. The range of the estimated RR from a power‐law Z ‐ R relationship can be as large as 50 mm hr −1 at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter ( D m ) along with radar reflectivity improves the estimated RR significantly.

AbstractVertical profiles of temperature, relative humidity, cloud particle concentration, median mass dimension, and mass content were derived using instruments on the NOAA P-3 aircraft for 37 spi...

Ice microphysics observations collected from eight flights into tropical cyclones (TCs) were analyzed to examine the performance of exponential versus gamma functions in representing the particle size distributions (PSDs) for cloud ice, snow, and graupel. Eighty‐four percent (87%) of cloud ice (snow) PSDs are above the correlation threshold of 0.9 between observations and the corresponding fitted curves by gamma fits, while only 43% (55%) of cloud ice (snow) PSDs by exponential fits. Sixteen percent of graupel PSDs are above the threshold by gamma fits but none by exponential fits. The intercept, slope, and shape in gamma functions are mutually dependent. When one among the three parameters is prescribed, the other two can be empirically rendered from the mutual‐dependence relationship. Counterintuitively, temperature does not play a conspicuous role in controlling ice PSDs in the TC environment but horizontal winds do, especially for snow, through the breakup process.

A novel Bayesian Monte Carlo integration (BMCI) technique was developed to retrieve geophysical variables from satellite microwave radiometer data in the presence of tropical cyclones. The BMCI technique includes three steps: generating a stochastic database, simulating satellite brightness temperatures using a radiative transfer model, and retrieving geophysical variables such as profiles of temperature, relative humidity, and cloud liquid and ice water content from real observations. The technique also provides uncertainty estimates for each retrieval and can output the error covariance matrix of selected parameters. The measurements from the Advanced Technology Microwave Sounder (ATMS) on board Suomi National Polar-Orbiting Partnership ( Suomi NPP ) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) were used as input. A new technique was developed to correct the ATMS and GMI observations for the beam-filling effect, which is due to small-scale variability of precipitation and clouds when compared with the instrument footprint and also the nonlinear relation between the brightness temperature and precipitation. In addition, the assimilation of the BMCI retrievals into the NASA GEOS model is discussed for Hurricane Maria. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns. Two possible factors that may limit the impact of the BMCI retrievals include 1) the resolution of the model (about 25 km), which was too coarse to show the potential of the BMCI data in improving the representation of tropical storms in the model forecast, and 2) the data assimilation system not being able to consider vertically correlated observation errors.

The roles of planetary and synoptic-scale waves in extreme cold wave (ECW) events over the southeastern (SE) and northwestern (NW) United States (US) are studied using a spherical harmonic decomposition in conjunction with piecewise tendency diagnosis (PTD). Planetary waves and synoptic waves jointly work together to initiate ECW events. Notably, the planetary waves not only provide a direct contribution to circulation field enacting ECW events but also alter the background circulation field in such a manner that promotes synoptic waves growth via increases in regional barotropic deformation. The SE-ECW events, concurrent with the Northern Hemisphere annular mode (NAM) negative phase, feature high latitude intensification and subsequent southeastward movement of cold surface air temperature (SAT) anomalies. The planetary-scale pattern provides a sizable contribution to the total wave pattern on both sea level pressure (SLP) and upper level. Moreover, the negative NAM planetary anomaly acts to displace the jet equatorward and thereby increases the barotropic deformation of the synoptic-scale anomaly over southeastern US. PTD confirms that the planetary-scale barotropic deformation plays a key role in deepening the negative height anomaly with a secondary contribution from baroclinic growth. In contrast, NW-ECW events feature a regional SAT cold anomaly that intensified in situ in association with a quasi-stationary positive SLP anomaly with a substantial planetary-scale wave component. The upper level circulation is characterized by a pronounced anomalous ridge over the Gulf of Alaska and a northeast-southwest tilted negative height anomaly to its east. The negative height anomaly axis is orthogonal to the planetary-scale dilatation, result in a stronger planetary barotropic deformation of the incipient negative height anomaly.

Intraseasonal modes of atmospheric variability over the Northern Hemisphere (NH) midlatitudes in boreal summer are identified via an empirical orthogonal function (EOF) analysis of the daily 10-90-day bandpass-filtered 250-hPa streamfunction for the period of 1950-2016. The first two EOF modes are characterized, respectively, by (i) a single-signed streamfunction anomaly that extends across the NH and (ii) a regional dipole structure with centers over the Aleutian Islands and northeastern Pacific. The third EOF mode (EOF-3) is a quasi-stationary wave train over the Pacific-North American sector with an equivalent barotropic structure in the vertical. EOF-3 is associated with a northwest-southeast oriented anomalous precipitation dipole over the United States. A nonmodal instability analysis of the boreal summer climatological flow in terms of the 250-hPa streamfunction reveals that one of the top optimal mode disturbances mimicking the EOF-3 structure grows from an initial precursor disturbance over East Asia through extracting kinetic energy from background flow and attains its maximum amplitude in around nine days. An additional lag regression analysis illustrates that anomalous latent heating associated with cloud and precipitation formation over East Asia is responsible for generating the precursor disturbance for the EOF-3-like optimal mode. This result suggests the existence of an important connection between the hydrological cycles of East Asia and North America, which is dynamically intrinsic to the boreal summer upper-tropospheric flow. Knowledge of such a connection will help us better understand and model hydroclimate variability over these two continents.

Although the occurrence of regional extreme weather events (EWEs) such as cold air outbreaks, heat waves, droughts and floods are known to be related to planetary climate modes (PCMs) such as the El-Nino Southern Oscillation or North Atlantic Oscillation, intermediate meteorological patterns (IMPs) such as jet stream blocking, cyclones, and high pressure systems are required to connect PCMs to EWEs. Our project has revealed a physical pathway for the interplay among EWEs, IMPs and PCMs. Under support of this award, we have characterized the behavior of IMPs that cause winter cold air outbreaks, extreme floods and warm season dry spells in the US. This includes formulating meteorological “fingerprints” for easily identifying IMPs. The research performed includes a parallel analysis of how well modern climate models are able to simulate IMP behavior. We have also developed a novel approach for characterizing PCM structures and identifying EWE-LMP-PCM linkages. We discovered that dry spells occurring during spring and summer over the US are affected by changes in the North Pacific Ocean temperature. We further revealed the physical origin of a critical jet stream pattern occurring over the North Pacific Ocean that provides a “bridge” between the water cycles of East Asia and North America, respectively. We developed an advanced diagnostic technique (called piecewise tendency diagnosis or PTD) to understand the physical causes for the IMP formation and EWE-LMP-PCM linkages.

Dual-polarization scanning radar measurements, air temperature soundings, and a polarimetric radarbased particle identification scheme are used to generate maps and probability density functions (PDFs) of the ice water path (IWP) in Hurricanes Arthur (2014) and Irene (2011) at landfall. The IWP is separated into the contribution from small ice (i. e., ice crystals), termed small-particle IWP, and large ice (i. e., graupel and snow), termed large-particle IWP. Vertically profiling radar data from Hurricane Arthur suggest that the small ice particles detected by the scanning radar have fall velocities mostly greater than 0.25ms 21 and that the particle identification scheme is capable of distinguishing between small and large ice particles in a mean sense. The IWP maps and PDFs reveal that the total and large-particle IWPs range up to 10 kgm 22, with the largest values confined to intense convective precipitation within the rainbands and eyewall. Small-particle IWP remains mostly,4 kgm 22, with the largest small-particle IWP values collocated with maxima in the total IWP. PDFs of the small-to-total IWP ratio have shapes that depend on the precipitation type (i. e., intense convective, stratiform, or weak-echo precipitation). The IWP ratio distribution is narrowest (broadest) in intense convective (weak echo) precipitation and peaks at a ratio of about 0.1 (0.3).

This study characterizes major modes of variability in the spring and summer U.S. dryness, as measured by the seasonal total number of dry (no-precipitation) days, and assesses the impact of the Pacific sea surface temperature. The most severe spring and summer dry conditions typically occur in the western United States. Maximum covariance analysis reveals that the Pacific Decadal Oscillation (PDO) is the primary driver of interannual variability in the U.S. dryness. El Nino-Southern Oscillation (ENSO) also contributes in part (especially during spring). Beyond the PDO and ENSO impact, interannual variations in spring dryness exhibit a meridional dipole structure, while variations during summer are related to a northwest-southeast (NW-SE) oriented dipole. The spring meridional dipole is associated with circulation anomalies resembling the West Pacific teleconnection pattern, while the summer NW-SE dipole is a downstream manifestation of a quasi-stationary wave train. A parallel analysis of Coupled Model Intercomparison Project's fifth phase models' historical simulations demonstrates that such models generally capture the relation between U.S. dryness and the PDO, albeit with varying degrees of accuracy. The models also show reasonable skill in simulating the residual meridional dipole in spring dryness variability but have difficulty representing the NW-SE oriented dipole occurring during summer. The model shortcomings isolated here largely arise from a misrepresentation of the corresponding large-scale circulation and moisture transport anomalies. These model biases suggest that great challenges exist in our ongoing pursuit of reliable projections of the U.S. hydroclimate variability.

Tropospheric planetary waves, often linked to asymmetries in lower boundary forcing, significantly modulate atmospheric blocking and storm track structures that are, in turn, linked to extreme surface weather events. Day‐to‐day variability in the planetary scale circulation, taken as wave numbers 1 to 5 in the daily 500 hPa geopotential heights, and the associated impact on storm tracks and regional weather are studied for the period 1950–2005. Six boreal cold‐season distinct planetary wave patterns are identified via hierarchical cluster analysis. The first, second, and sixth patterns feature a prominent zonal wave number 1 structure, while the fourth and fifth patterns resemble the negative and positive phases of northern annular mode, respectively. The second pattern represents an amplification of the climatological mean wave structure, while the third pattern resembles the zonal wave number 3 pattern. A multitaper spectral analysis of the daily projection indices indicates that the planetary wave patterns are primarily intraseasonal in nature. The first (sixth) pattern combines the positive (negative) phase of the Pacific‐North American teleconnection pattern and negative (positive) phase of the North Atlantic Oscillation, inducing poleward (equatorward) shifts in the Pacific storm track and a weakened (strengthened) Atlantic storm track. In contrast, the fourth (fifth) pattern results in a simultaneous equatorward (poleward) displacement of both storm tracks. Extreme cold waves over the continental United States (U.S.) are favored during occurrences of the fourth and sixth patterns. During episodes of the first pattern, increased rainfall prevails over much of the U.S., while precipitation anomalies induced by the other patterns are more regional in nature.

Warm season dry spells over the central and eastern United States are classified into three canonical types via a hierarchical cluster analysis for the period 1950-2005. Four CMIP5 models exhibit diverging skill in representing the observed behavior, ranging from southern Great Plains dry spells that are reasonably simulated by all four models to southeastern U.S. dry spells that are only accurately captured by one model. A model's skill in representing a particular dry spell cluster is positively correlated with the model's ability to simulate the large-scale meteorological patterns (LMPs) accompanying the dry spell. The interannual variability and overall observed decreasing trend in dry spell days are represented with varying degrees of accuracy by the four models. The results 1) highlight existing shortcomings in the climate model representation of regional dry spells and 2) illustrate the importance of properly simulating the observed spectrum of LMPs in minimizing these shortcomings.

Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950-2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a "ase function'' for evaluating a climate model's potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U. S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model's skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4' s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.

Regional spring onset events are identified within four high-latitude sectors: the primary (critical) region over North Siberia (CR), Greenland-North America (G-NA), East Asia (EA), and Alaska (AL). To identify the primary forcing of the rapid temperature increases observed during spring onset, the contributions to the near-surface air temperature anomaly tendency are diagnosed within the thermodynamic equation for each of the four regional event categories. For each region, anomalous eddy heat flux convergence is the primary contributor to regional warming prior to, and during the early stages of, spring onset (through day +5). Thereafter, horizontal advection of the climatological-mean temperature by the large-scale circulation anomaly field emerges as the leading contributor to regional warming (during the later stages of spring onset). A parallel diagnostic of storm track strength (using the envelope function) reveals a systematic weakening of eddy activity within each region, leading to a reduction in the northward eddy heat flux out of the domain and an accumulation of heat within the region. For CR, G-NA, and EA events, an east-west dipole structure in the sea level pressure anomaly field, with lower (higher) pressure to the west (east), generates the anomalous southerly flow linked to late period linear warm advection. Our results indicate that anomalous dynamical processes associated with synoptic eddy activity and stationary wave patterns are the primary contributors to rapid temperature increase during Arctic spring onset events, with minimal contributions from anomalous diabatic processes.

Extreme cold waves (ECWs) occurring over the conterminous United States (US) are studied through a systematic identification and documentation of their local synoptic structures, associated large-scale meteorological patterns (LMPs), and forcing mechanisms external to the US. Focusing on the boreal cool season (November–March) for 1950‒2005, a hierarchical cluster analysis identifies three ECW patterns, respectively characterized by cold surface air temperature anomalies over the upper midwest (UM), northwestern (NW), and southeastern (SE) US. Locally, ECWs are synoptically organized by anomalous high pressure and northerly flow. At larger scales, the UM LMP features a zonal dipole in the mid-tropospheric height field over North America, while the NW and SE LMPs each include a zonal wave train extending from the North Pacific across North America into the North Atlantic. The Community Climate System Model version 4 (CCSM4) in general simulates the three ECW patterns quite well and successfully reproduces the observed enhancements in the frequency of their associated LMPs. La Niña and the cool phase of the Pacific Decadal Oscillation (PDO) favor the occurrence of NW ECWs, while the warm PDO phase, low Arctic sea ice extent and high Eurasian snow cover extent (SCE) are associated with elevated SE-ECW frequency. Additionally, high Eurasian SCE is linked to increases in the occurrence likelihood of UM ECWs.

During winter, anomalous temperature regimes (ATRs), which include cold-air outbreaks (CAOs) and warm waves (WWs), have important impacts in the southeastern United States. This study provides a synoptic-dynamic characterization of ATRs in the southeastern United States from 1949 to 2011 through composite time-evolution analyses. Events are categorized by the sign and amplitude of relevant low-frequency modes. During CAO (WW) onset, negative (positive) geopotential height anomalies are observed in the upper troposphere over the Southeast with oppositely signed anomalies in the lower troposphere over the central United States. In most cases, there is a surface east-west geopotential height anomaly dipole, with anomalous northerly (CAO) or southerly (WW) flow into the Southeast leading to cold or warm surface air temperature anomalies, respectively. Companion potential vorticity anomaly analyses reveal prominent features in the mid- to upper troposphere consistent with the coincident geopotential height anomaly patterns. Ultimately, synoptic-scale disturbances are found to serve as dynamic triggers for ATR events, while low-frequency modes provide a favorable environment for ATR onset. The results provide a qualitative indication of the role of low-frequency modes in ATR onset. In WW (CAO) events influenced by low-frequency modes, the North American geopotential height anomaly pattern arises in part as a downstream (regional) manifestation of the negative Pacific-North American pattern (North Atlantic Oscillation). Interestingly, the North Atlantic Oscillation contributes to both CAO onset and demise. Thus, these results indicate that low-frequency modes also affect event duration (CAOs). One general distinction found for ATRs is that CAOs involve substantial airmass transport while WW formation is more regional in nature.