OceanRAIN—the Ocean Rainfall And Ice-phase precipitation measurement Network—provides in-situ along-track shipboard data of precipitation, evaporation and the resulting freshwater flux at 1-min resolution over the global oceans from June 2010 to April 2017. More than 6.83 million minutes with 75 parameters from 8 ships cover all routinely measured atmospheric and oceanographic state variables along with those required to derive the turbulent heat fluxes. The precipitation parameter is based on measurements of the optical disdrometer ODM470 specifically designed for all-weather shipboard operations. The rain, snow and mixed-phase precipitation occurrence, intensity and accumulation are derived from particle size distributions. Additionally, microphysical parameters and radar-related parameters are provided. Addressing the need for high-quality in-situ precipitation data over the global oceans, OceanRAIN-1.0 is the first comprehensive along-track in-situ water cycle surface reference dataset for satellite product validation and retrieval calibration of the GPM (Global Precipitation Measurement) era, to improve the representation of precipitation and air-sea interactions in re-analyses and models, and to improve understanding of water cycle processes over the global oceans.

Forecasts of marine cold air outbreaks critically rely on the interplay of multiple parameterisation schemes to represent sub-grid scale processes, including shallow convection, turbulence, and microphysics. Even though such an interplay has been recognised to contribute to forecast uncertainty, a quantification of this interplay is still missing. Here, we investigate the tendencies of temperature and specific humidity contributed by individual parameterisation schemes in the operational weather prediction model AROME-Arctic. From a case study of an extensive marine cold air outbreak over the Nordic Seas, we find that the type of planetary boundary layer assigned by the model algorithm modulates the contribution of individual schemes and affects the interactions between different schemes. In addition, we demonstrate the sensitivity of these interactions to an increase or decrease in the strength of the parameterised shallow convection. The individual tendencies from several parameterisations can thereby compensate each other, sometimes resulting in a small residual. In some instances this residual remains nearly unchanged between the sensitivity experiments, even though some individual tendencies differ by up to an order of magnitude. Using the individual tendency output, we can characterise the subgrid-scale as well as grid-scale responses of the model and trace them back to their underlying causes. We thereby highlight the utility of individual tendency output for understanding process-related differences between model runs with varying physical configurations and for the continued development of numerical weather prediction models.

Forecast errors in near-surface temperatures are a persistent issue for numerical weather prediction models. A prominent example is warm biases during cloud-free, snow-covered nights. Many studies attribute these biases to parametrized processes such as turbulence or radiation. Here, we focus on the contribution of physical processes to the nocturnal temperature development. We compare model timestep output of individual tendencies from parametrized processes in the weather prediction model AROME-Arctic to measurements from Sodankylä, Finland. Thereby, we differentiate between the weakly stable boundary layer (wSBL) and the very stable boundary layer (vSBL) regimes. The wSBL is characterized by continuous turbulent exchange within the near-surface atmosphere, causing near-neutral temperature profiles. The vSBL is characterized by a decoupling of the lowermost model level, low turbulent exchange, and very stable temperature profiles. In our case study, both regimes occur simultaneously on small spatial scales of about 5 km. In addition, we demonstrate the model’s sensitivity towards an updated surface treatment, allowing for faster surface cooling. The updated surface parametrization has profound impacts on parametrized processes in both regimes. However, only modelled temperatures in the vSBL are impacted substantially, whereas more efficient surface cooling in the wSBL is compensated by an increased turbulent heat transport within the boundary layer. This study demonstrates the utility of individual tendencies for understanding process-related differences between model configurations and emphasizes the need for model studies to distinguish between the wSBL and vSBL for reliable model verification.

<p>Numerical weather prediction (NWP) models generally display comparatively low predictive skill in the Arctic. Particularly, the large impact of sub-grid scale, parameterised processes, such as surface fluxes, radiation or cloud microphysics during high-latitude weather events pose a substantial challenge for numerical modelling. Such processes are most influential during mesoscale weather events, such as polar lows, often embedded in cold air outbreaks (CAO), some of which cause high impact weather. Uncertainty in Arctic weather forecasts is thus critically dependent on parameterised processes. The strong influence from several parameterised processes also makes model forecasts particularly susceptible to compensation of errors from different parameterisations, which potentially limits model improvement.<br>Here we analyse model output of individual parameterised tendencies of wind, temperature and humidity during Arctic high-impact weather in AROME-Arctic, the operational NWP model used by the Norwegian Meteorological Institute Norway for the European Arctic. Individual tendencies describe the contribution of each applied physical parameterisation to a respective variable per model time step. We study a CAO-event taking place during 24 - 27 December 2015. This intense and widespread CAO event, reaching from the Fram Straight to Norway and affecting a particularly large portion of the Nordic seas at a time, was characterised by strong heat fluxes along the sea ice edge. <br>Model intern definitions for boundary layer type become apparent as a decisive factor in tendency contributions. Especially the interplay between the dual mass flux and the turbulence scheme is of essence here. Furthermore, sensitivity experiments, featuring a run without shallow convection and a run with a new statistical cloud scheme, show how a physically similar result is obtained by substantially different tendencies in the model.</p>

<p>We present results from a set of field campaigns conducted in an arctic valley and fjord environment in central Spitsbergen, Svalbard. These field campaigns, which are conducted as part of a graduate class at the University Centre in Svalbard (UNIS), address a range of phenomena typical for the arctic atmospheric boundary layer using both observational and numerical means. These phenomena include low-level jets, cold pools, drainage flows, and air-sea interactions, several of which typically are challenging to accurately model. On the observational side, we utilise a range of sensors and instrumentation platforms, such as portable weather stations, a tethersonde (anchored weather balloon), small temperature sensors (TinyTags), sonic anemometers, automatic weather stations, and drones. As of this year, the sensor suite will also constitute a wind lidar and a microwave temperature profiler. The resulting datasets represent a unique model-independent data set from a region where observations are otherwise sparse. On the numerical side, we utilise data from the high-resolution (2.5 km horizontal grid spacing) AROME-Arctic weather prediction model. AROME Arctic is run operationally by the Norwegian Meteorological Institute (MET Norway) for a domain covering Northern Fennoscandia, larger parts of the Barents Sea, and Svalbard. We use the model data both to plan our fieldwork and for interpreting our observations. In turn, we use the observations for improving our understanding of the mentioned phenomena and also for validating the model.</p>

<p>Stochastic parameterisations are an important way to represent uncertainty in the deterministic forecasting models underlying ensemble prediction systems. In many of the currently used stochastic parameterisation approaches, random generators produce correlation patterns that induce spatially and temporally coherent perturbations to the parameterisation parameters or tendencies. The patterns that are currently used in the Harmonie ensemble prediction system are therefore unrelated to the atmospheric flow or weather situation. Here we investigate the potential of replacing such random patterns by accumulated tendency fields from parameterized physical processes in the model. The rationale hereby is that by perturbing the parameterisations with a field that reflects where parameterisations are most active, rather than a random pattern, the model obtains a more targeted increase in the degrees-of-freedom to represent forecasting uncertainty.</p><p>As an initial test case, we consider a large cold-air outbreak during 23-25 Dec 2015 that affected large parts of Scandinavia. During that time period, strong heat fluxes persisted near the ice edge, while widespread shallow convection dominated in the center of the model domain. For diagnosing the perturbation fields, we utilise an implementation of individual tendency diagnostics implemented in AROME-Arctic within the ALERTNESS project. Total physical tendencies for the horizontal wind components, for air temperature and humidity are accumulated with a time filtering throughout the 66 h forecast period.</p><p>The accumulated tendencies from all parameterisations for the different variables show overlapping and differing centers of activity. Wind parameterisations are active near the ice edge, and with smaller scale variability over land areas, in particular at lower model levels. Temperature tendency patterns show activity that is more confined to the ice edge, and a narrow coastal stripe along Northern Scandinavia. These first results show that the approach provides spatially coherent patterns of parameterisation activity, which are meaningfully related to the dominating weather situation. Based on sensitivity tests of cloud parameterisation parameters in a single-column version, we outline the next steps in the path towards diagnostic perturbation patterns for stochastically perturbed perturbations in the Harmonie EPS system.</p>

<p>Stochastic parameterisations are an important way to represent uncertainty in the deterministic forecasting models underlying ensemble prediction systems. Current stochastic parameterisation approaches use random correlation patterns that are unrelated to the atmospheric flow to induce coherent perturbations to parameterisations. Here we replace these patterns by accumulated tendency fields from parameterized physical processes in the HARMONIE-AROME system. Our rationale is that by perturbing the parameterisations with a field that reflects where parameterisations are most active, rather than random, the model obtains a more targeted increase in the degrees-of-freedom to represent forecasting uncertainty.</p> <p>Here we study a large marine cold-air outbreak over the Norwegian Sea. Strong heat fluxes persisted near the ice edge, and shallow convection dominated in the center of the model domain. Perturbation fields are diagnosed from individual tendency diagnostics implemented in AROME-Arctic within ALERTNESS. Total physical tendencies for the horizontal winds, for temperature and humidity are accumulated with a time filtering throughout the 66 h forecast period.</p> <p>Accumulated tendencies show overlapping and differing centers of activity. Wind parameterisations are active near the ice edge, and with smaller scale variability over land areas. Temperature tendency patterns show activity more confined to the ice edge, and the coast of northern Scandinavia. Such spatially coherent patterns of parameterisation activity are meaningfully related to current weather. To exploit the relation between parameterisation activity and weather patterns for ensemble perturbation, we conduct sensitivity tests of cloud parameterisation parameters in a single-column model version MUSC and the full model version. First results illustrate our progress towards the use of diagnostic perturbation patterns for stochastically perturbed perturbations in the HarmonEPS system.</p>

<div> <p><span>Convection is a major contributor to the overturning of heat, moisture, and momentum in the atmospheric boundary layer and is responsible for the formation of convective clouds and precipitation. However, the characteristic properties, the dynamics, and the processes that trigger and shape the development of atmospheric convection are still only sparsely sampled. In this study, we present an approach to probe and characterise atmospheric convection from both the Eulerian and the Lagrangian perspectives, utilising dual-doppler lidar observations combined with velocity estimates from paraglider and sailplane flight trajectories. Some of the evaluated flights involve additional sensors to sample temperature and humidity. The observations are obtained over the mountainous terrain of southwestern Norway. As a proof-of-concept, we demonstrate the capability of the dual-doppler lidar setup to accurately characterise atmospheric convection and to validate the complementing estimates from the flight tracks in complex terrain. The Lidar setup accurately resolves dynamic properties of the convective circulation with high detail, while the flight tracks resolve the dynamic (and static) properties of the convective updrafts. </span><span> </span></p> </div>