In Feb. 2017, a five-day sequence of atmospheric river storms in California, USA, resulted in extreme inflows to Lake Oroville, the state’s second-largest reservoir. Damage to the reservoir’s spillway infrastructure necessitated evacuation of 188,000 people; subsequent infrastructure repairs cost $1 billion. We assess the atmospheric conditions, snowmelt, and runoff against major historical events. The event generated exceptional runoff volumes (second-largest in a 30 year record) partially at odds with the event precipitation totals (ninth-largest). We explain the discrepancy with observed record melt of deep antecedent snowpack, heavy rainfall extending to unusually high elevations, and high water vapor transport during the atmospheric river storms. An analysis of distributed snow water equivalent indicates that snowmelt increased water available for runoff watershed-wide by 37% (25-52% at 90% confidence). The results highlight an acute flood risk to public safety and infrastructure projected to increase in severity in a warmer and more variable climate.

Clouds play an essential role in the global energy budget but the impact of anthropogenic aerosols on clouds is still poorly understood. We use fifteen-minute temporal resolution geostationary satellite data to study the temporal evolution of polluted cloud tracks detected in the European part of Russia. Previous analysis of polluted cloud tracks shows that cloud water response to aerosols is bidirectional. Here, we show that the day-night contrast in cloud responses partly explains the bidirectional cloud water responses. We have data only for sunlight hours, but we can interpret the cloud responses detected already since the early morning as night-time responses. On average, the decrease in cloud water offsets 46% of the Twomey effect in the study area while the decrease happens during night-time, probably due to aerosol-enhanced entrainment. In the afternoon, cloud water is more likely to increase in the polluted clouds, most probably due to suppressed precipitation. Our findings highlight the need to better account for the temporal evolution of cloud responses to estimate the aerosol radiative forcing more accurately.

Coastal flooding is one of the most costly and deadly natural hazards facing the US Mid-Atlantic region today. Impacts in this heavily populated and economically significant region are caused by a combination of the location’s exposure and natural forcing from storms and sea-level rise. Tropical cyclones (TCs) and mid-latitude (ML) weather systems each have caused extreme coastal flooding in the region. Skew surge was computed over each tidal cycle for the past 40 years (1980 – 2019) at several tide gauges in the Delaware and Chesapeake Bays to compare the meteorological component of surge for each weather type. Although TCs cause higher mean surges, ML weather systems can produce surges just as severe and occur much more frequently, peaking in the cold season (Nov – Mar). Of the top 10 largest surge events, TCs account for 30-45% in the Delaware and upper Chesapeake Bays and 40–45% in the lower Chesapeake Bay. This percentage drops to 10-15% for larger numbers of events in all regions. Mean sea-level pressure and 500 hPa geopotential height (GPH) fields of the top 10 surge events from ML weather systems show a low-pressure center west-southwest of Delmarva and a semi-stationary high-pressure center to the northeast prior to maximum surge, producing strong easterly winds. Low-pressure centers intensify under upper-level divergence as they travel eastward, and the high-pressure centers are near the GPH ridges. During lower bay events, the low-pressure centers develop further south, intensifying over warmer coastal waters, with a south-shifted GPH pattern compared to upper bay events.

Thirteen years of precipitation d2H, d18O, and 3H data for three western United States continental interior weather stations, supplemented with 60 years of precipitation data, have been analyzed. The stations are located 1,000 to 2,000 km from four ocean moisture sources. Precipitation was evaluated relative to storm track trajectory, the El Niño-Southern Oscillation Oceanic Niño Index (INO), orography, precipitation amount, air temperature, month, and season. The INO was not fond to correlate with precipitation flux or isotopic composition. Tritium deposition was evaluated relative to the ‘spring leak’, thunderstorms, surface evaporation, storm tracks, and seasons. Local meteoric water lines and the Global Meteoric Water Line were compared. Winter precipitation is isotopically depleted and summer precipitation is isotopically enriched. Factors affecting the stable isotopes include winter cold cloud temperature, summer rain droplet partial evaporation, gradual rain out, and multiple episodes of soil moisture re-evaporation and subsequent re-precipitation.

Global-scale Observations of Limb and Disk (GOLD) disk measurements of far ultraviolet molecular nitrogen band emissions are used to retrieve temperatures (T$_{disk}$), which are representative of lower thermospheric altitudes. The present investigation studies the response of lower thermospheric temperatures to geomagnetic activities of varying magnitudes. In this study, it has been observed that T$_{disk}$ increases over all latitudes in response to enhanced geomagnetic activity. The increase in temperature is proportional to the strength of the geomagnetic activity and is greater at higher latitudes. Temperature enhancements vary from 10s to 100s of Kelvins from low- to mid-latitudes. Local time behavior shows that pre-noon enhancements in temperatures, during relatively stronger geomagnetic activities, are greater compared to afternoon, which can be attributed to the combined action of daytime dynamics and geomagnetic forcing. This study thus demonstrates the utility of GOLD T$_{disk}$ measurements investigating the effects of dynamical and external forcings in the thermosphere.

Shallow cloud fields over the subtropical ocean exhibit many spatial patterns. The frequency of occurrence of these patterns can change under global warming. Hence, they may influence subtropical marine clouds’ climate feedback. While numerous metrics have been proposed to quantify cloud patterns, a systematic, widely accepted description is still missing. Therefore, this paper suggests one. We compute 21 metrics for 5000 satellite scenes of shallow clouds over the subtropical Atlantic Ocean and translate the resulting dataset to its principal components (PCs). This yields a unimodal, continuous distribution without distinct classes, whose first four PCs explain 82% of all 21 metrics’ variance. The PCs correspond to four interpretable dimensions: Characteristic length, void size, directional alignment and horizontal cloud-top height variance. These dimensions span a space in which an effective pattern description can be given, which may be used to better understand the patterns’ underlying physics and feedback on climate.

Coastal vegetated habitats like seagrass meadows can mitigate anthropogenic carbon emissions by sequestering CO2 as “blue carbon” (BC). Already, some coastal ecosystems are actively managed to enhance BC storage, with associated BC stocks included in national greenhouse gas inventories or traded on international markets. However, the extent to which BC burial fluxes are enhanced or counteracted by other carbon fluxes, especially air-water CO2 flux (FCO2) remains poorly understood. To this end, we synthesized all available direct FCO2 measurements over seagrass meadows made using a common method (atmospheric Eddy Covariance), across a globally-representative range of ecotypes. Of the four sites with seasonal data coverage, two were net CO2 sources, with average FCO2 equivalent to 44 - 115% of the global average BC burial rate. At the remaining sites, net CO2 uptake was 101 - 888% of average BC burial. A wavelet coherence analysis demonstrates that FCO2 was most strongly related to physical factors like temperature, wind, and tides. In particular, tidal forcing appears to shape global-scale patterns in FCO2, likely due to a complex suite of drivers including: lateral carbon exchange, bottom-driven turbulence, and pore-water pumping. Lastly, sea-surface drag coefficients were always greater than prediction for the open ocean, supporting a universal enhancement of gas-transfer in shallow coastal waters. Our study points to the need for a more comprehensive approach to BC assessments, considering not only organic carbon storage, but also air-water CO2 exchange, and its complex biogeochemical and physical drivers.

Land skin temperature (LST) is one of the most important factors in the land-atmosphere interaction process. Raw measured LSTs may contain biases due to instrument replacement, changes in recording procedures, and other nonclimatic factors. This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 to 2017. The high-quality land surface air temperature (LSAT) dataset is used to correct the LST warming biases in cold months in regions north of 40ºN due to the replacement of observation instruments around 2004. Subsequently, the Multiple Analysis of Series for Homogenization (MASH) method is adopted to detect and then adjust the daily observed LST records. In total, 3.68×103 significant breakpoints in 1.65×106 monthly records are detected. A large number of these significant breakpoints are located over large parts of the Sichuan Basin and southern China. After MASH procedure, LSTs at more than 80% of the breakpoints are adjusted within +/- 0.5 ºC, and 10% of the breakpoints are adjusted over 1.5 ºC. Compared to the raw LST dataset over the whole domain, the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations. Finally, we preliminarily analyze the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend (0.22 ºC decadal-1). The homogenized LST dataset can be further adopted for a variety of applications (e.g., model evaluation and extreme event characterization).

A rocket-triggered lightning flash containing negative–positive–negative current polarity reversal during its initial stage is analyzed using multiple synchronized observation data. The flash was triggered under a thunderstorm transition zone between the convective region and the stratiform region. Both positive leaders developing in the transition zone and negative leaders developing toward the convective region could be identified. As the negative initial continuous current (ICC) declined, a negative leader was transformed from a recoil leader which turned to break down virgin air off the preconditioned positive leader branch. As the negative leader developing forward, a reactivated breakdown leader bridging the grounding trunk channel and the initiation region of the negative leader caused the current polarity reversed from negative to positive 0.22 ms later, which is reported for the first time. The negative leader channel terminated after propagating for 71.08 ms, and the ICC reversed to be negative again owing to the propagation of another positive branch. The horizontal dipole charge structure contributed to the branching of positive leader and the initiation of negative leader, which combined to produce the upward bipolar lightning. During the positive ICC stage, both positive and negative channels simultaneously contributed to the channel-base current and several negative recoil leaders injecting negative charge to the grounding trunk channel produced a fast decrease of the current.

We analyzed OH airglow images observed from an all-sky camera at King Sejong Station, Antarctica for the period of 2012–2016. Using M-transform method, 2D-power spectra of short period waves (< 1 hr) were obtained from 107 image sequences. The power spectral densities evidently show that the mesospheric wave activity is the strongest during winter. We also constructed climatological wind blocking diagrams using horizontal winds obtained from MERRA-2 for the altitudes of = 10–64 km, and from KSS meteor radar data for = 80–90 km. The wind blocking diagrams are negatively matched with the dominant propagating directions of the observed slow speed waves (< 30 m/s), providing the graphical evidence of wind filtering effects. However, there are significant eastward waves in winter and strong south-eastward waves in spring that are not blocked by the stratospheric winds. We speculate that these waves may be generated from the upper stratosphere or mesosphere.

Biogenic emission models are developed on the foundation of leaf physiological processes and driven by a set of physical and biological factors. To estimate emissions online, many studies used weather forecasting models coupled with simple biogenic emission algorithms, in which the canopy physiological parameters were neglected or oversimplified. In this study, the land surface scheme CLM4 (Community Land Model version 4) coupled in the advanced Weather Research and Forecasting model (WRF) was used to determine canopy physiological parameters. The MEGAN (Model of Emissions of Gases and Aerosols from Nature) algorithms embedded in CLM4 scheme used these parameters to estimate biogenic emissions. The emission estimated by using leaf temperature in our study were about 23% higher than that based on air temperature as in the previous methods. Compared with studies neglecting shaded canopy, the separate treatments of sunlit and shaded leaves in this study lowered the estimations by a factor of 2 through decreasing diffuse radiaton absorbed by sunlit canopy. Dynamic weather history was used in our study to replace the fixed values in the original MEGAN-CLM4 code. An emission inventory of isoprene and monoterpenes in China was established for the year 2018. The estimates were evaluated against field measurements. Generally, the coupled model produced a reasonable simulation in both emission budgets and spatiotemporal distribution of biogenic emissions.

Mixed-phase clouds (MPCs), which consist of both supercooled cloud droplets and ice crystals, play an important role in the Earth’s radiative energy budget and hydrological cycle. In particular, the fraction of ice crystals in MPCs determines their radiative effects, precipitation formation and lifetime. In order for ice crystals to form in MPCs, ice-nucleating particles (INPs) are required. However, a large-scale relationship between INPs and ice initiation in clouds has yet to be observed. By analyzing satellite observations of the typical transition temperature (T*) where MPCs become more frequent than liquid clouds, we constrain the importance of INPs in MPC formation. We find that over the Arctic and Southern Ocean, snow and sea ice cover significantly reduces T*. This indicates that the availability of INPs is essential in controlling cloud phase evolution and that local sources of INPs in the high-latitudes play a key role in the formation of MPCs.

We present here an in-depth analysis of one time interval when quasi-linear mirror mode structures were detected by magnetic field and plasma measurements as observed by the NASA/Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. We employ ion and electron spectrometers in tandem to support the magnetic field measurements and confirm that the signatures are indeed mirror modes. Wedged against the magnetic pile-up boundary, the low-frequency signatures lasted on average ~10 s with corresponding sizes of the order of 15-30 upstream solar wind proton thermal gyroradii, or 10-20 proton gyroradii in the immediate wake of the quasi-perpendicular bow shock. Their peak-to-peak amplitudes were of the order of 30-35 nT with respect to the background field, and appeared as a mixture of dips and peaks, suggesting that they may have been at different stages in their evolution. Situated in a marginally stable plasma with β|| ~ 1, we hypothesise that these so-called magnetic bottles, containing a relatively higher energy and denser ion population with respect to the background plasma, were formed upstream of the spacecraft behind the quasi-perpendicular shock. These signatures are very reminiscent of magnetic bottles found at other unmagnetised objects such as Venus and comets, also interpreted as mirror modes. Our case study constitutes the first unambiguous detection of mirror modes around Mars, which had up until now only been surmised because of the lack of high-temporal resolution plasma measurements.

We develop a hierarchy of simplified ocean models for coupled ocean, atmosphere, and sea ice climate simulations using the Community Earth System Model version 1 (CESM1). The hierarchy has four members: a slab ocean model, a mixed-layer model with entrainment and detrainment, an Ekman mixed-layer model, and an ocean general circulation model (OGCM). Flux corrections of heat and salt are applied to the simplified models ensuring that all hierarchy members have the same climatology. We diagnose the needed flux corrections from auxiliary simulations in which we restore the temperature and salinity to the daily climatology obtained from a target CESM1 simulation. The resulting 3-dimensional corrections contain the interannual variability fluxes that maintain the correct vertical gradients of temperature and salinity in the tropics. We find that the inclusion of mixed-layer entrainment and Ekman flow produces sea surface temperature and surface air temperature fields whose means and variances are progressively more similar to those produced by the target CESM1 simulation. We illustrate the application of the hierarchy to the problem of understanding the response of the climate system to the loss of Arctic sea ice. We find that the shifts in the positions of the mid-latitude westerly jet and of the Inter-tropical Convergence Zone (ITCZ) in response to sea-ice loss depend critically on upper ocean processes. Specifically, heat uptake associated with the mixed-layer entrainment influences the shift in the westerly jet and ITCZ. Moreover, the shift of ITCZ is sensitive to the form of Ekman flow parameterization.

High altitude platforms (HAPs) such as stratospheric balloons and eventually other high altitude, long endurance unmanned vehicles have reached a stage where it is possible to deploy a persistent fleet of aircraft acting as a meteorological observation network for a reasonable cost. Whether directly collecting in situ measurements like winds aloft or via dropsondes or performing remote sensing using, for example, radar or GPS radio occultation, these observation networks can collect measurements which are hard to obtain from other observation platforms and are complementary to other systems. They are also highly autonomous and can be deployed worldwide (and thus can add redundancy to the global forecast system). Because they are mobile, the observation network can be adjusted to collect in situ measurements in the places that are most important to forecasters and scientists. We use simulation of fleets of stratospheric balloons that are navigated by machine learning algorithms that actuate an altitude control system to demonstrate some of the potential constellations that are achievable with HAPs and motivate the greater consideration of an autonomous, persistent HAPs-based meteorological observing network.

Climate data from Earth System Models are increasingly being used to study the impacts of climate change on a broad range of biogeophysical (forest fires, fisheries, etc.) and human systems (reservoir operations, urban heat waves, etc.). Before this data can be used to study many of these systems, post-processing steps commonly referred to as bias correction and statistical downscaling must be performed. “Bias correction” is used to correct persistent biases in climate model output and “statistical downscaling” is used to increase the spatiotemporal resolution of the model output (i.e. 1 deg to 1/16th deg grid boxes). For our purposes, we’ll refer to both parts as “downscaling”. In the past few decades, the applications community has developed a plethora of downscaling methods. Many of these methods are ad-hoc collections of post processing routines while others target very specific applications. The proliferation of downscaling methods has left the climate applications community with an overwhelming body of research to sort through without much in the form of synthesis guiding method selection or applicability. Motivated by the pressing socio-environmental challenges of climate change – and with the learnings from previous downscaling efforts in mind – we have begun working on a community-centered open framework for climate downscaling: scikit-downscale. We believe that the community will benefit from the presence of a well-designed open source downscaling toolbox with standard interfaces alongside a repository of benchmark data to test and evaluate new and existing downscaling methods. In this notebook, we provide an overview of the scikit-downscale project, detailing how it can be used to downscale a range of surface climate variables such as air temperature and precipitation. We also highlight how scikit-downscale framework is being used to compare existing methods and how it can be extended to support the development of new downscaling methods.

Solar-Induced chlorophyll Fluorescence (SIF) provides a powerful proxy for determining forest gross primary production (GPP), particularly in evergreen ecosystems where traditional measures of greenness fail. The dynamics of the SIF/GPP relationship, however, are poorly understood under varying viewing directions and light conditions. This is, in large part, due to challenges in measuring SIF at the spatiotemporal scale that is necessary to understand these effects. Therefore, the aim of this work is to utilize high-temporal and spatial resolution SIF measurements to better constrain the response of SIF to ambient canopy illumination and viewing geometry. We use a PhotoSpec instrument and eddy covariance measurements to explore the SIF/GPP relationship under various viewing directions and light conditions during the 2019 and 2020 growing seasons at the Old Black Spruce site in Saskatchewan, Canada. PhotoSpec is a tower-based 2-D scanning spectrometer system capable of taking Fraunhofer-line based SIF retrievals in the red and far-red wavelength ranges with a 0.7 degree field of view at a ~30 second time resolution. Measured SIF and GPP are combined with SCOPE modelling results to provide a mechanistic understanding of the physical and ecophysiological drivers for the SIF/GPP relationship in the Boreal Forest. Our results show that viewing direction and solar zenith/azimuth angles are important for the SIF signal under direct light conditions, but not under diffuse. Furthermore, the SIF/GPP relationship changes under direct and diffuse light conditions at a 30 minute, daily, and monthly resolution. Our ability to use SIF as a proxy for GPP depends on a quantitative understanding of radiative transfer within the canopy and how scanning geometry impacts SIF measurements. These results provide an important insight into these relationships in the Boreal forest, a region where GPP has been traditionally difficult to track using remote sensing.

Following collapses of the neighbouring Larsen A and B ice shelves, Larsen C has become a focus of increased attention. Determining how the prevailing meteorological conditions influence the surface melt regime is of paramount significance for understanding the dominant processes causing melt and ultimately for predicting its future. A new, high-resolution (4 km grid spacing) Met Office Unified Model (MetUM) hindcast of atmospheric conditions and surface melt processes over the central Antarctic Peninsula during the period 1998-2017 is developed for this purpose. The hindcast is capable of reliably simulating observed near-surface meteorology and surface melt conditions over Larsen C. In contrast with previous model simulations, the MetUM captures the observed east-west gradient in surface melting associated with foehn winds, as well as the inter-annual variability in melt shown in previous observational studies. The hindcast is applied to two case studies – the months preceding the collapse of the Larsen B ice shelf in March 2002 and the high-foehn, high-melt period of March-May 2016 - to test its ability to reproduce the atmospheric effects that contributed to considerable melting during those periods. The results suggest that the MetUM hindcast is a reliable tool with which to explore the dominant causes of surface melting on Larsen C.

We investigate the meteorological and dynamical conditions that led to the extreme heat in the Pacific Northwest from late June to early July 2021. The extreme heat was preceded by an upper-level atmospheric blocking that snatched a warm pool of air from lower latitudes. A heat-trapping stable stratification ensued within the block, raising the surface temperatures significantly. An upper-tropospheric wave breaking and the concomitant surface cyclogenesis off the coast of Alaska initiated the block formation. The regional local wave activity budget reveals that a localized diabatic source associated with this storm critically contributed to the block by enhancing the zonal wave activity flux downstream, whose convergence over Canada drove the blocking. A simple model-based reconstruction predicts a 41 percent reduction in strength and a 10-degree eastward displacement of the block when the upstream diabatic source is reduced by just 30 percent.

From a research data repositories’ perspective, offering data management services in-line with the FAIR principles is becoming more and more of a selling point to compete on the market. In order to do so, the services offered must be evaluated and credited following transparent and credible procedures. Several FAIRness evaluation methods are openly available for being applied to archived (meta)data. However, there exists no standardized and globally accepted FAIRness testing procedure to date. Here, we apply an ensemble of 5 FAIRness evaluation approaches to selected datasets archived in the WDCC. The selection represents the majority of WDCC-archived datasets (by volume) and reflects the entire spectrum of data curation levels. Two tests are purely automatic, two are purely manual and one test applies a hybrid method (manual and automatic combined) for evaluation. The results of our evaluation show a mean FAIR score of 0.67 of 1. Manual approaches show higher scores than automated ones. The hybrid approach shows the highest score. Computed statistics show agreement between the tests at the data collection level. None of the five evaluation approaches is fully fit-for-purpose to evaluate (discipline-specific) FAIRness, but all have their merit. Manual testing captures domain- and repository-specific aspects of FAIR. Machine-actionability of archived (meta)data is judged by the evaluator. Automatic approaches evaluate the machine-actionable features of archived (meta)data. These have to be accessible by an automated agent and comply with globally established standards. An evaluation of contextual metadata (essential for reusability) is not possible. Correspondingly, the hybrid method combines the advantages and eliminates the deficiencies of manual and automatic evaluation. We recommend that future operational FAIRness evaluation be based on a mature hybrid approach. The automatic part of the evaluation would retrieve and evaluate as much machine-actionable discipline specific (meta)data content as possible and be then complemented by a manual evaluation focusing on the contextual aspects of FAIR. Design and adoption of the discipline-specific aspects will have to be conducted in concerted community efforts. We illustrate a possible structure of this process with an example from climate research.

In operational analyses of the surface moisture imbalance that defines drought, the supply aspect has generally been well characterized by precipitation; however, the same count be said of the demand side—a function of evaporative demand (E0) and surface moisture availability. In drought monitoring, E0 is often poorly parameterized by a climatological mean, by non-physically based estimates, or is neglected entirely. One problem has been a paucity of driver data—on temperature, humidity, solar radiation, and wind speed—required to fully characterize E0. This deficient E0 modeling is particularly troublesome over data-sparse regions that are also home to drought-vulnerable populations, such as across much of Africa. There is thus urgent need for global E0 estimates for physically accurate drought analyses and food security assessments; further we need an improved understanding of how E0 and drought interact and to exploit these interactions in drought monitoring. In this presentation we explore ways to meet these needs. From MERRA-2—an accurate, fine-resolution land-surface/atmosphere reanalysis—we have developed a >38-year, daily, global Penman-Monteith reference ET dataset as a fully physical metric of E0. This dataset is valuable for examining hydroclimatic changes and extremes. A novel drought index based on this dataset—the Evaporative Demand Drought Index (EDDI)—represents drought’s demand perspective, and permits early warning and ongoing monitoring of agricultural flash drought and hydrologic drought. We highlight the findings of our examination of E0-drought interactions and using EDDI in Africa. Using reference ET as an E0 metric has permitted explicit attribution of the variability of E0 across Africa, and of E0 anomalies associated with canonical droughts in the Sahel region. This analysis determines where, when, and to what relative degree each of the individual drivers of E0 affects the demand side of drought. Using independent estimates of drought across space and time—CHIRPS precipitation and the Normalized Difference Vegetation Index for 1982-2015—we examine the differences between drought and non-drought periods, and between precipitation-forced droughts and droughts forced by a combination of precipitation and E0.

One of the key small-scale processes that necessarily require parameterisation is the turbulent mixing of cloudy and cloud-free air, i.e. entrainment and detrainment (in the following simplified as entrainment), which describe the in-mixing of ambient air into the cloud and the mixing of cloudy air into the environment surrounding the cloud, respectively. Entrainment changes cloud particle properties such as number concentrations and sizes, which also modifies the radiative properties of the cloud, and has important implications for cloud lifetime. No reliable formulation exists to date that allows understanding and describing entrainment in terms of cloud- and environmental physical quantities. This is despite the fact that a wide variety of entrainment parameterisations exists. However, their dependencies on meteorological parameters remain controversial. Indeed, the mixing processes (including entrainment) and their treatment in the numerical models have been found to be responsible for much of the large spread found in climate sensitivity estimates. A combination of measurements in the turbulent wind tunnel LACIS-T at TROPOS and computational fluid dynamics (CFD) simulations is used to identify the main parameters that govern the strength of entrainment. Here, we will present first results from the observations in LACIS-T, where the two air streams of the wind tunnel are used to mimic in-cloud and out-of-cloud conditions. Conditions in one of the air streams are varied to test the impact of the corresponding parameters (i.e. temperature, relative humidity, air speed) on the entrainment. Cloud droplets are induced by a droplet generator. The measurements are accompanied by CFD simulations which are verified by the point measurements in the wind tunnel and allow to retrieve full 3D fields.

Clear air turbulence (CAT) is a serious threat for civil aviation flights. In the years 2009 through 2015, with the support of the European Commission, as a part of the 7th Framework Program, the DELICAT project (DEmonstration of LIdar based Clear Air Turbulence detection) was implemented. In this project, an air-borne lidar was developed and built for an early detection of CAT. In August 2013, flight tests of the lidar were carried out. During the experiments, a large unique data set was collected; a copy of the data set is in the possession of the Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences (IAP RAS). In the proposed project, we propose solving the following problems. We will develop a numerical model of the laser sounding of CAT, considering the effect of the laser beam propagation in a random medium, as well as the aerosol (Mie) and molecular (Rayleigh) scattering. The analysis of the existing observations indicates that, in most cases, the multiple scattering effects are negligible. The will allow us to develop the model on the basis of the multiple phase screen method. This model will produce realizations of the random signal for specific realization of the random refractivity and aerosol distribution fields. A modification of the multiple phase screen method will allow modeling a diverging laser beam. This will also allow modeling of the back-scattering enhancement (BSE). By varying the aerosol composition, it will be possible to model different degree of correlation and intensity ratio in co- and cross-polarization channels. The model of the laser sounding of CAT will help answering the question, whether aerosol is an impeding factor when using the BSE effect. We will develop a realistic model of the observation geometry variations. We will perform the primary processing of the whole available data set of the DELICAT observations. This will allow us to estimate the statistical properties of the measurement noise. We will analyze the DELICAT observations. We will fit the model parameters in order to reproduce, as close as possible, the statistical properties of the observations. This will help answering the questions, how accurately and timely it is possible to detect CAT parameters using two types of lidar systems: 1) system with two polarization channels, and 2) system based on the BSE effect.

The accurate prediction of rainfall, and in particular rainfall extremes, remains challenging for numerical weather prediction models. This can be attributed to subgrid-scale parameterizations of processes that play a crucial role in the multi-scale dynamics, as well as the strongly intermittent nature and the highly skewed, non-Gaussian distribution of rainfall. Here we show that a specific type of deep neural networks can learn rainfall extremes from a numerical weather prediction ensemble. A frequency-based weighting of the loss function is proposed to enable the learning of extreme values in the distributions' tails. We apply our framework in a post-processing step to correct for errors in the model-predicted rainfall. Our method yields a much more accurate representation of relative rainfall frequencies and improves the forecast skill of extremes by factors ranging from two to above six, depending on the event magnitude.