Development and application of a backscatter lidar forward operator for quantitative validation of aerosol dispersion models and future data assimilation

A new backscatter lidar forward operator was developed which is based on the distinct calculation of the aerosols’ backscatter and extinction properties. The forward operator was adapted to the COSMO-ART ash dispersion simulation of the Eyjafjallajökull eruption in 2010. While the particle number concentration was provided as a model output variable, the scattering properties of each individual particle type were determined by dedicated scattering calculations. Sensitivity studies were performed to estimate the uncertainties related to the assumed particle properties. Scattering calculations for several types of non-spherical particles required the usage of T-matrix routines. Due to the distinct calculation of the backscatter and extinction properties of the models’ volcanic ash size classes, the sensitivity studies could be made for each size class individually, which is not the case for forward models based on a fixed lidar ratio. Finally, the forward-modeled lidar profiles have been compared to automated ceilometer lidar (ACL) measurements both qualitatively and quantitatively while the attenuated backscatter coefficient was chosen as a suitable physical quantity. As the ACL measurements were not calibrated automatically, their calibration had to be performed using satellite lidar and ground-based Raman lidar measurements. A slight overestimation of the model-predicted volcanic ash number density was observed. Major requirements for future data assimilation of data from ACL have been identified, namely, the availability of calibrated lidar measurement data, a scattering database for atmospheric aerosols, a better representation and coverage of aerosols by the ash dispersion model, and more investigation in backscatter lidar forward operators which calculate the backscatter coefficient directly for each individual aerosol type. The introduced forward operator offers the flexibility to be adapted to a multitude of model systems and measurement setups

Quantitative precipitation estimation based on highresolution numerical weather prediction and data assimilation with WRF - a performance test

Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale bandlike precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data

Characterisation of boundary layer turbulent processes by the Raman lidar BASIL in the frame of HD(CP) 2 Observational Prototype Experiment

Abstract. Measurements carried out by the University of Basilicata Raman lidar system (BASIL) are reported to demonstrate the capability of this instrument to characterise turbulent processes within the convective boundary layer (CBL). In order to resolve the vertical profiles of turbulent variables, high-resolution water vapour and temperature measurements, with a temporal resolution of 10 s and vertical resolutions of 90 and 30 m, respectively, are considered. Measurements of higher-order moments of the turbulent fluctuations of water vapour mixing ratio and temperature are obtained based on the application of autocovariance analyses to the water vapour mixing ratio and temperature time series. The algorithms are applied to a case study (11:30–13:30 UTC, 20 April 2013) from the High Definition Clouds and Precipitation for Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE), held in western Germany in the spring 2013. A new correction scheme for the removal of the elastic signal crosstalk into the low quantum number rotational Raman signal is applied. The noise errors are small enough to derive up to fourth-order moments for both water vapour mixing ratio and temperature fluctuations.To the best of our knowledge, BASIL is the first Raman lidar with a demonstrated capability to simultaneously retrieve daytime profiles of water vapour turbulent fluctuations up to the fourth order throughout the atmospheric CBL. This is combined with the capability of measuring daytime profiles of temperature fluctuations up to the fourth order. These measurements, in combination with measurements from other lidar and in situ systems, are important for verifying and possibly improving turbulence and convection parameterisation in weather and climate models at different scales down to the grey zone (grid increment  ∼  1 km; Wulfmeyer et al., 2016).For the considered case study, which represents a well-mixed and quasi-stationary CBL, the mean boundary layer height is found to be 1290 ± 75 m above ground level (a.g.l.). Values of the integral scale for water vapour and temperature fluctuations at the top of the CBL are in the range of 70–125 and 75–225 s, respectively; these values are much larger than the temporal resolution of the measurements (10 s), which testifies that the temporal resolution considered for the measurements is sufficiently high to resolve turbulent processes down to the inertial subrange and, consequently, to resolve the major part of the turbulent fluctuations. Peak values of all moments are found in the interfacial layer in the proximity of the top of the CBL. Specifically, water vapour and temperature second-order moments (variance) have maximum values of 0.29 g2 kg−2 and 0.26 K2; water vapour and temperature third-order moments have peak values of 0.156 g3 kg−3 and −0.067 K3, while water vapour and temperature fourth-order moments have maximum values of 0.28 g4 kg−4 and 0.24 K4. Water vapour and temperature kurtosis have values of  ∼  3 in the upper portion of the CBL, which indicate normally distributed humidity and temperature fluctuations. Reported values of the higher-order moments are in good agreement with previous measurements at different locations, thus providing confidence in the possibility of using these measurements for turbulence parameterisation in weather and climate models.In the determination of the temperature profiles, particular care was dedicated to minimise potential effects associated with elastic signal crosstalk on the rotational Raman signals. For this purpose, a specific algorithm was defined and tested to identify and remove the elastic signal crosstalk and to assess the residual systematic uncertainty affecting temperature measurements after correction. The application of this approach confirms that, for the present Raman lidar system, the crosstalk factor remains constant with time; consequently an appropriate assessment of its constant value allows for a complete removal of the leaking elastic signal from the rotational Raman lidar signals at any time (with a residual error on temperature measurements after correction not exceeding 0.18 K)

Observation of sensible and latent heat flux profiles with lidar

We present the first measurement of the sensible heat flux (H) profile in the convective boundary layer (CBL) derived from the covariance of collocated vertical-pointing temperature rotational Raman lidar and Doppler wind lidar measurements. The uncertainties of the H measurements due to instrumental noise and limited sampling are also derived and discussed. Simultaneous measurements of the latent heat flux profile (L) and other turbulent variables were obtained with the combination of water-vapor differential absorption lidar (WVDIAL) and Doppler lidar. The case study uses a measurement example from the HOPE (HD(CP)$^{2}$ Observational Prototype Experiment) campaign, which took place in western Germany in 2013 and presents a cloud-free well-developed quasi-stationary CBL. The mean boundary layer height z$_{i}$ was at 1230 m above ground level. The results show – as expected – positive values of H in the middle of the CBL. A maximum of (182±32) W m$^{-2}$, with the second number for the noise uncertainty, is found at 0.5 z$_{i}$. At about 0.7 z$_{i}$, H changes sign to negative values above. The entrainment flux was (−62±27) W m$^{-2}$. The mean sensible heat flux divergence in the observed part of the CBL above 0.3 z$_{i}$ was −0.28 W m$^{-3}$, which corresponds to a warming of 0.83 K h$^{-1}$. The L profile shows a slight positive mean flux divergence of 0.12 W m$^{-3}$ and an entrainment flux of (214±36) W m$^{-2}$. The combination of H and L profiles in combination with variance and other turbulent parameters is very valuable for the evaluation of large-eddy simulation (LES) results and the further improvement and validation of turbulence parameterization schemes

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide pro- files of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular de- struction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used

Statistical Analysis of Simulated Spaceborne Thermodynamics Lidar Measurements in the Planetary Boundary Layer

The performance of a spaceborne Raman lidar offering measurements of water vapor, temperature, aerosol backscatter and extinction is assessed statistically by use of a lidar simulator and a global model to provide inputs for simulation. The candidate thermodynamics lidar system is envisioned to make use of a sun-synchronous, dawn/dusk orbit. Cloud-free atmospheric profiles simulated by the NASA/GSFC GEOS model for the orbit of the CALIPSO satellite on 15 July 2009 were used as input to a previously validated lidar simulator where GEOS profiles that satisfy the solar zenith angle restrictions of the dawn/dusk orbit, and are located within the Planetary Boundary Layer as defined by the GEOS model, were selected for the statistical analysis. To assess the performance of the simulated thermodynamics lidar system, measurement goals were established by considering the WMO Observing Systems Capability Analysis and Review (OSCAR) requirements for Numerical Weather Prediction. The efforts of Di Girolamo et al., 2018 established the theoretical basis for the current work and discussed many of the technological considerations for a spaceborne thermodynamics lidar. The work presented here was performed during 2017–2018 under the auspices of the NASA/GSFC Planetary Boundary Layer Science Task Group and expanded on previous efforts by considerably increasing the statistical robustness of the performance simulations and extending the statistics to include those of aerosol backscatter and extinction measurements. Further work that is currently being conducted includes Observing Systems Simulation Experiments to assess the impact of a thermodynamics lidar on global forecast improvement

Inductive biases in deep learning models for weather prediction

Deep learning has recently gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based weather prediction (DLWP) models have made significant progress in the last few years, achieving forecast skills comparable to established numerical weather prediction (NWP) models with comparatively lesser computational costs. In order to train accurate, reliable, and tractable DLWP models with several millions of parameters, the model design needs to incorporate suitable inductive biases that encode structural assumptions about the data and modelled processes. When chosen appropriately, these biases enable faster learning and better generalisation to unseen data. Although inductive biases play a crucial role in successful DLWP models, they are often not stated explicitly and how they contribute to model performance remains unclear. Here, we review and analyse the inductive biases of six state-of-the-art DLWP models, involving a deeper look at five key design elements: input data, forecasting objective, loss components, layered design of the deep learning architectures, and optimisation methods. We show how the design choices made in each of the five design elements relate to structural assumptions. Given recent developments in the broader DL community, we anticipate that the future of DLWP will likely see a wider use of foundation models -- large models pre-trained on big databases with self-supervised learning -- combined with explicit physics-informed inductive biases that allow the models to provide competitive forecasts even at the more challenging subseasonal-to-seasonal scales

Land–Atmosphere Interactions: The LoCo Perspective

Land–atmosphere (L-A) interactions are a main driver of Earth’s surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land–Atmosphere System Study (GLASS) panel has supported “L-A coupling” as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hot spots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local Land–Atmosphere Coupling (LoCo) project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges

Land-Atmosphere Interactions: The LoCo Perspective

Land-atmosphere (L-A) interactions are a main driver of Earth's surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence of extremes such as drought. Despite their importance, the representation of L-A interactions in weather and climate models remains poorly constrained, as they involve a complex set of processes that are difficult to observe in nature. In addition, a complete understanding of L-A processes requires interdisciplinary expertise and approaches that transcend traditional research paradigms and communities. To address these issues, the international Global Energy and Water Exchanges project (GEWEX) Global Land-Atmosphere System Study (GLASS) panel has supported 'L-A coupling' as one of its core themes for well over a decade. Under this initiative, several successful land surface and global climate modeling projects have identified hotspots of L-A coupling and helped quantify the role of land surface states in weather and climate predictability. GLASS formed the Local L-A Coupling ('LoCo') project and working group to examine L-A interactions at the process level, focusing on understanding and quantifying these processes in nature and evaluating them in models. LoCo has produced an array of L-A coupling metrics for different applications and scales, and has motivated a growing number of young scientists from around the world. This article provides an overview of the LoCo effort, including metric and model applications, along with scientific and programmatic developments and challenges