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Holger Siebert, Kai-Erik Szodry, Ulrike Egerer, Birgit Wehner, Silvia Henning, Karine Chevalier, Janine Lückerath, Oliver Welz, Kay Weinhold, Felix Lauermann, Matthias Gottschalk, André Ehrlich, Manfred Wendisch, Paulo Fialho, Greg Roberts, Nithin Allwayin, Simeon Schum, Raymond A. Shaw, Claudio Mazzoleni, Lynn Mazzoleni, Jakub L. Nowak, Szymon P. Malinowski, Katarzyna Karpinska, Wojciech Kumala, Dominika Czyzewska, Edward P. Luke, Pavlos Kollias, Robert Wood, and Juan Pedro Mellado

Abstract

We report on the Azores Stratocumulus Measurements of Radiation, Turbulence and Aerosols (ACORES) campaign, which took place around Graciosa and Pico Islands/Azores in July 2017. The main objective was to investigate the vertical distribution of aerosol particles, stratocumulus microphysical and radiative properties, and turbulence parameters in the eastern North Atlantic. The vertical exchange of mass, momentum, and energy between the free troposphere (FT) and the cloudy marine boundary layer (MBL) was explored over a range of scales from submeters to kilometers. To cover these spatial scales with appropriate measurements, helicopter-borne observations with unprecedented high resolution were realized using the Airborne Cloud Turbulence Observation System (ACTOS) and Spectral Modular Airborne Radiation Measurement System–Helicopter-Borne Observations (SMART-HELIOS) instrumental payloads. The helicopter-borne observations were combined with ground-based aerosol measurements collected at two continuously running field stations on Pico Mountain (2,225 m above sea level, in the FT), and at the Atmospheric Radiation Measurement (ARM) station on Graciosa (at sea level). First findings from the ACORES observations we are discussing in the paper are as follows: (i) we have observed a high variability of the turbulent cloud-top structure on horizontal scales below 100 m with local temperature gradients of up to 4 K over less than 1 m vertical distance, (ii) we have collected strictly collocated radiation measurements supporting the relevance of small-scale processes by revealing significant inhomogeneities in cloud-top brightness temperature to scales well below 100 m, and (iii) we have concluded that aerosol properties are completely different in the MBL and FT with often-complex stratification and frequently observed burst-like new particle formation.

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Adam Eshel, Hagit Messer, Harald Kunstmann, Pinhas Alpert, and Christian Chwala

Abstract

Using signal level measurements from commercial microwave links (CMLs) has proven to be a valuable tool for near-ground 2-D rain mapping. Such mapping is commonly based on spatial interpolation methods, where each CML is considered as a point measurement instrument located at its center. The validity of the resulted maps is tested against radar observations. However, since radar has limitations, accuracy of CML-based reconstructed rain maps remains unclear. Here we provide a quantitative comparison of the performance of CML-based spatial interpolation methods for rain mapping by conducting a systematic analysis: first by quantifying the performance of maps generated from semi-synthetic CML data, and thereafter turning to real-data analysis of the same rain events. A radar product of the GermanWeather Service, serves as ground truth for generating semi-synthetic data, in which several temporal aggregations of the radar rainfall fields are used to create different decorrelation distances. The study was done over an area of 225X245 km2 in southern Germany, with 808 CMLs. We compare the performance of two spatial interpolation methods - Inverse Distance Weighting and Ordinary Kriging - in two cases: where each CML is represented as a single point, and where three points are used. The points’ measurements values in the latter are determined using an iterative algorithm. The analysis of both cases is based on a 48 hour rain event. The results re-confirm the validity of CML-based rain retrieval, showing a slight systematic performance improvement when an iterative algorithm is applied so each CML is represented by more than a single point, independent of the interpolation method.

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Yonatan Givon, Chaim I. Garfinkel, and Ian White

Abstract

An intermediate complexity General Circulation Model is used to investigate the transient response of the NH winter stratosphere to modulated ultraviolet (UV) radiation by imposing a step-wise, deliberately exaggerated UV perturbation and analyzing the lagged response. Enhanced UV radiation is accompanied by an immediate warming of the tropical upper stratosphere. The warming then spreads into the winter subtropics due to an accelerated Brewer Dobson Circulation in the tropical upper stratosphere. The poleward meridional velocity in the subtropics leads to an increase in zonal wind in midlatitudes between 20N and 50N due to Coriolis torque. The increase in mid-latitude zonal wind is accompanied by a dipole in Eliassen-Palm flux convergence, with decreased convergence near the winter pole and increased convergence in mid-latitudes (where winds are strengthening due to the Coriolis torque); this dipole subsequently extends the anomalous westerlies to subpolar latitudes within the first ten days. The initial radiatively-driven acceleration of the Brewer-Dobson circulation due to enhanced shortwave absorption is replaced in the subpolar winter stratosphere by a wave-driven deceleration of the Brewer-Dobson circulation, and after a month the wave-driven deceleration of the Brewer-Dobson circulation encompasses most of the winter stratosphere. Approximately a month after UV is first modified, a significant poleward jet shift is evident in the troposphere. The results of this study may have implications for the observed stratospheric and tropospheric responses to solar variability associated with the 27-day solar rotation period, and also to solar variability on longer timescales.

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Kian Abbasnezhadi, Alain N. Rousseau, Étienne Foulon, and Stéphane Savary

Abstract

Sparse precipitation information can result in uncertainties in hydrological modelling practices. Precipitation observation network augmentation is one way to reduce the uncertainty. Meanwhile, in basins with snowpack-dominated hydrology, in the absence of a high-density precipitation observation network, assimilation of in situ and remotely sensed measurements of snowpack state variables can also provide the possibility to reduce flow estimation uncertainty. Similarly, assimilation of existing precipitation observations into gridded numerical precipitation products can alleviate the adverse effects of missing information in poorly instrumented basins. In Canada, the Regional Deterministic Precipitation Analysis (RDPA) data from the Canadian Precipitation Analysis (CaPA) system have been increasingly applied for flow estimation in sparsely gauged Nordic basins. Moreover, CaPA-RDPA data have also been applied to establish observational priorities for augmenting precipitation observation networks. However, the accuracy of the assimilated data should be validated before being applicable in observation network assessment. The assimilation of snowpack state variables has proven to significantly improve streamflow estimates, and therefore, it can provide the benchmark against which the impact of assimilated precipitation data on streamflow simulation can be compared. Therefore, this study introduces a parsimonious framework for performing a proxy-validation of the precipitation assimilated products through the application of snow assimilation in physically-based hydrologic models. This framework is demonstrated to assess the observation networks in three boreal basins in Yukon, Canada. The results indicate that in most basins, the gridded analysis products generally enjoyed the level of accuracy required for accurate flow simulation and therefore were applied in the meteorological network assessment in those cases.

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Jason Giovannettone

Abstract

Because many locations throughout the United States have recently experienced periods of extreme wet and dry conditions, an attempt is made to better understand the relationships between long-term total precipitation and climate variability. Correlations between total precipitation at over 1200 U.S. sites and low-frequency oscillations of the mean activity of 30 hydroclimate indices (HCIs) are analyzed using correlation analysis and sliding window sizes on the order of years to reduce the effects of high-frequency variability in the time series. The strength and significance of each relationship are assessed using the Pearson’s correlation coefficient r, leave-one-out cross validation, and a Monte Carlo approach. The sliding window size, lag time, and beginning month were varied to produce the optimal correlation at each site; a 60-month sliding window and lag times of 12 and 48 months resulted in the strongest correlations. Correlations with 7 and 8 HCIs at each lag time, respectively, were regionally delineated. The Madden–Julian oscillation represents the dominant HCI at the 12-month lag time throughout most of the western half of the United States, whereas El Niño–Southern Oscillation revealed strong links to annual and longer-term total precipitation in the eastern and western United States, respectively. Other HCIs, such as the North Atlantic Oscillation and the Pacific decadal oscillation, demonstrated dominance over much smaller and more well-defined regions within the Southwest and the South, respectively. The final results of this study allow a greater understanding of potential links between climate variability and long-term precipitation in the United States, leading to potentially improved predictions of the onset and persistence of future extreme meteorological events at longer lead times.

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Sean Celona, Sophia T. Merrifield, Tony de Paolo, Nate Kaslan, Tom Cook, Eric J. Terrill, and John A. Colosi

Abstract

A method based on machine learning and image processing techniques has been developed to track the surface expression of internal waves in near-real time. X-Band radar scans are first preprocessed and averaged to suppress surface wave clutter and enhance the signal to noise ratio of persistent backscatter features driven by gradients in surface currents. A machine learning algorithm utilizing a support vector machine (SVM) model is then used to classify whether or not the image contains an internal solitary wave (ISW) or internal tide bore (bore). The use of machine learning is found to allow rapid assessment of the large data set, and provides insight on characterizing optimal environmental conditions to allow for radar illumination and detection of ISWs and bores. Radon transforms and local maxima detections are used to locate these features within images that are determined to contain an ISWor bore. The resulting time series of locations is used to create a map of propagation speed and direction that captures the spatiotemporal variability of the ISW or bore in the coastal environment. This technique is applied to 2 months of data collected near Point Sal, California and captures ISW and bore propagation speed and direction information that currently cannot be measured with instruments such as moorings and synthetic aperture radar (SAR).

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Biao Zhang, Yiru Lu, William Perrie, Guosheng Zhang, and Alexis Mouche

Abstract

We have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This data set is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that RR-pol radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to RH-, RV-, and RL-pol. Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, CMODRR are in good agreement with buoy measurements, with root mean square errors of 1.38, 1.51, 1.47, 1.25 m/s, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

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Mark R Jury and América R. Gaviria Pabón

Abstract

Satellite and reanalysis products are used to study the atmospheric environment, aerosols and trace gases in smoke plumes over South America in the period 2000-2018. Climatic conditions and fire density maps provide context to link biomass burning across the southern Amazon (5-15S, 50-70W) to thick near-surface plumes of trace gases and fine aerosols. Intra-seasonal weather patterns that underpin greater fire emissions in the dry season (Jul-Oct) are exacerbated by high pressure over a cool east Pacific, for example in September 2007. Smoke plume dispersion simulated with HYSPLIT reveal a slowing of westward transport between sources in eastern Brazil and the Andes Mountains. During cases of thick smoke plumes over the southern Amazon, an upper ridge and sinking motions confine trace gases and fine aerosols below 4 km. Long-term warming tends to coincide with the zone of biomass burning are +0.03C/yr in the air and +0.1C/yr at the land surface. Our study suggests that weather conditions promoting fire emissions also tend to limit dispersion.

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Dereka Carroll-Smith, Robert J. Trapp, and James M. Done

Abstract

The overarching purpose of this study is to investigate the impacts of anthropogenic climate change both on the rainfall and tornadoes associated with tropical cyclones (TCs) making landfall in the U.S. Atlantic basin. The “pseudo–global warming” (PGW) approach is applied to Hurricane Ivan (2004), a historically prolific tropical cyclone tornado (TCT)-producing storm. Hurricane Ivan is simulated under its current climate forcings using the Weather Research and Forecasting Model. This control simulation (CTRL) is then compared with PGW simulations in which the current forcings are modified by climate-change differences obtained from the Community Climate System Model, version 4 (NCAR); Model for Interdisciplinary Research on Climate, version 5 (MIROC); and Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL). Changes in TC intensity, TC rainfall, and TCT production, identified for the PGW-modified Ivan, are documented and analyzed. Relative to CTRL, all three PGW simulations show an increase in TC intensity and generate substantially more accumulated rainfall over the course of Ivan’s progression over land. However, only one of the TCs under PGW (MIROC) produced more TCTs than CTRL. Evidence is provided that, in addition to favorable environmental conditions, TCT production is related to the TC track length and to the strength of the interaction between the TC and an environmental midlevel trough. Enhanced TCT generation at landfall for MIROC and GFDL is attributed to increased values of convective available potential energy, low-level shear, and storm-relative environmental helicity.

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Sujan Pal, Francina Dominguez, María Eugenia Dillon, Javier Alvarez, Carlos Marcelo Garcia, Stephen W. Nesbitt, and David Gochis

Abstract

Some of the most intense convective storms on Earth initiate near the Sierras de Córdoba mountain range in Argentina. The goal of the RELAMPAGO field campaign was to observe these intense convective storms and their associated impacts. The intense observation period (IOP) occurred during November–December 2018. The two goals of the hydrometeorological component of RELAMPAGO IOP were 1) to perform hydrological streamflow and meteorological observations in previously ungauged basins and 2) to build a hydrometeorological modeling system for hindcast and forecast applications. During the IOP, our team was able to construct the stage–discharge curves in three basins, as hydrological instrumentation and personnel were successfully deployed based on RELAMPAGO weather forecasts. We found that the flood response time in these river locations is typically between 5 and 6 h from the peak of the rain event. The satellite-observed rainfall product IMERG-Final showed a better representation of rain gauge–estimated precipitation, while IMERG-Early and IMERG-Late had significant positive bias. The modeling component focuses on the 48-h simulation of an extreme hydrometeorological event that occurred on 27 November 2018. Using the Weather Research and Forecasting (WRF) atmospheric model and its hydrologic component WRF-Hydro as an uncoupled hydrologic model, we developed a system for hindcast, deterministic forecast, and a 60-member ensemble forecast initialized with regional-scale atmospheric data assimilation. Critically, our results highlight that streamflow simulations using the ensemble forecasting with data assimilation provide realistic flash flood forecast in terms of timing and magnitude of the peak. Our findings from this work are being used by the water managers in the region.

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