ALBERT

Description: Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal-resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g. variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O–B). Monitoring of O–B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O–B analysis for MWR observations in clear-sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective-scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O–B monitoring can effectively detect instrument malfunctions. O–B statistics (bias, standard deviation, and root mean square) for water vapour channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line centre ( ∼  2–2.5 K) towards the high-frequency wing ( ∼  0.8–1.3 K). Statistics for zenith and lower-elevation observations show a similar trend, though values increase with increasing air mass. O–B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54–58 GHz). Opaque channels show lower uncertainties (

Description: Central Europe has a vital and extensive meteorological research community comprising national weather services, universities, and research organizations and institutes. Nearly all of them are involved in the open scientific questions regarding clouds and precipitation processes. The research activities include observations (from in situ ground-based remote sensing radio soundings to satellite-based observations), model development on all scales (from direct numerical simulations to global climate models), and other activities. With Germany as an example our first objective is to show the large amount and the diversity of observations regarding clouds and precipitation. The goal is to give an overview of existing measurements and datasets to show the benefit of combining the different information from a variety of observations. Up to now the access to and the usage of these datasets from different sources was not straightforward, due to the issue of missing data and archiving standards for observational data. This then motivates our second objective, which is to introduce our solution for this issue—the novel Standardized Atmospheric Measurement Data archive (SAMD). SAMD is one of the outcomes of the German research initiative High Definition Clouds and Precipitation for Advancing Climate Prediction [HD(CP)2]. The goal of SAMD is an easy-to-use approach for both data producers and archive users. Therefore the archive provides observational data in the common Climate Forecast (CF) Conventions format and makes it available to the broader public. SAMD offers highly standardized quality-controlled data and metadata for a wide range of instruments, with open access, which makes this novel archive important for the research community.

Description: Ground-based microwave radiometers (MWRs) offer the capability to provide continuous, high-temporal resolution observations of the atmospheric thermodynamic state in the planetary boundary layer (PBL) with low maintenance. This makes MWR an ideal instrument to supplement radiosonde and satellite observations when initializing numerical weather prediction (NWP) models through data assimilation. State-of-the-art data assimilation systems (e.g., variational schemes) require an accurate representation of the differences between model (background) and observations, which are then weighted by their respective errors to provide the best analysis of the true atmospheric state. In this perspective, one source of information is contained in the statistics of the differences between observations and their background counterparts (O-B). Monitoring of O-B statistics is crucial to detect and remove systematic errors coming from the measurements, the observation operator, and/or the NWP model. This work illustrates a 1-year O-B analysis for MWR observations in clear sky conditions for an European-wide network of six MWRs. Observations include MWR brightness temperatures (TB) measured by the two most common types of MWR instruments. Background profiles are extracted from the French convective scale model AROME-France before being converted into TB. The observation operator used to map atmospheric profiles into TB is the fast radiative transfer model RTTOV-gb. It is shown that O-B monitoring can effectively detect instrument malfunctions. O-B statistics (bias, standard deviation and root-mean-square) for water vapor channels (22.24–30.0 GHz) are quite consistent for all the instrumental sites, decreasing from the 22.24 GHz line center (~ 2–2.5 K) towards the high-frequency wing (~ 0.8–1.3 K). Statistics for zenith and lower elevation observations show a similar trend, though values increase with increasing air mass. O-B statistics for temperature channels show different behaviour for relatively transparent (51–53 GHz) and opaque channels (54-58 GHz). Opaque channels show lower uncertainties (

Description: The height of the atmospheric boundary layer or mixing layer is an important parameter for understanding the dynamics of the atmosphere and the dispersion of trace gases and air pollution. The height of the mixing layer (MLH) can be retrieved, among other methods, from lidar or ceilometer backscatter data. These instruments use the vertical backscatter lidar signal to infer MLHL, which is feasible because the main sources of aerosols are situated at the surface and vertical gradients are expected going from the aerosol loaded mixing layer close to the ground to the cleaner free atmosphere above. Various lidar/ceilometer algorithms are currently applied, but accounting for MLH temporal development is not always well taken care of. As a result, MLHL retrievals may jump between different atmospheric layers, rather than reliably track true MLH development over time. This hampers the usefulness of MLHL time series for e.g. process studies, model validation/verification and climatology. Here, we introduce a new method "Pathfinder", which applies graph theory to simultaneously evaluate timeframes consistent with scales of MLH dynamics, leading to coherent tracking of MLH. Starting from a grid of gradients in the backscatter profiles, MLH development is followed using Dijkstra's shortest path algorithm (Dijkstra, 1959). Locations of strong gradients are connected under the condition that subsequent points on the path are limited to a restricted vertical range. The search is further guided by rules based on presence of clouds and residual layers. Applied to backscatter lidar data from Cabauw, excellent agreement is found with windprofiler retrievals for a 12-day period in 2008 (R2 = 0.90) and visual judgment of lidar data during a full year in 2010 (R2 = 0.96). These values compare favourably against other MLHL methods applied to the same lidar data set and corroborate more consistent MLH tracking by Pathfinder.

Description: Vertical profiles of the aerosol particles hygroscopic properties, their mixing state as well as chemical composition were measured above northern Italy and the Netherlands. An aerosol mass spectrometer (AMS; for chemical composition) and a white-light humidified optical particle spectrometer (WHOPS; for hygroscopic growth) were deployed on a Zeppelin NT airship within the PEGASOS project. This allowed one to investigate the development of the different layers within the planetary boundary layer (PBL), providing a unique in situ data set for airborne aerosol particles properties in the first kilometre of the atmosphere. Profiles measured during the morning hours on 20 June 2012 in the Po Valley, Italy, showed an increased nitrate fraction at  ∼  100 m above ground level (a.g.l.) coupled with enhanced hygroscopic growth compared to  ∼  700 m a. g. l. This result was derived from both measurements of the aerosol composition and direct measurements of the hygroscopicity, yielding hygroscopicity parameters (κ) of 0.34  ±  0.12 and 0.19  ±  0.07 for 500 nm particles, at  ∼  100 and  ∼  700 m a. g. l., respectively. The difference is attributed to the structure of the PBL at this time of day which featured several independent sub-layers with different types of aerosols. Later in the day the vertical structures disappeared due to the mixing of the layers and similar aerosol particle properties were found at all probed altitudes (mean κ ≈ 0.18  ±  0.07). The aerosol properties observed at the lowest flight level (100 m a. g. l.) were consistent with parallel measurements at a ground site, both in the morning and afternoon. Overall, the aerosol particles were found to be externally mixed, with a prevailing hygroscopic fraction. The flights near Cabauw in the Netherlands in the fully mixed PBL did not feature altitude-dependent characteristics. Particles were also externally mixed and had an even larger hygroscopic fraction compared to the results in Italy. The mean κ from direct measurements was 0.28 ±  0.10, thus considerably higher than κ values measured in Italy in the fully mixed PBL.