Assimilation of Ground-Based Microwave Radiometer on Heavy Rainfall Forecast in Beijing

Ground-based microwave radiometers (MWRPS) can provide continuous atmospheric temperature and relative humidity profiles for a weather prediction model. We investigated the impact of assimilation of ground-based microwave radiometers based on the rapid-refresh multiscale analysis and prediction system-short term (RMAPS-ST). In this study, five MWRP-retrieved profiles were assimilated for the precipitation enhancement that occurred in Beijing on 21 May 2020. To evaluate the influence of their assimilation, two experiments with and without the MWRPS assimilation were set. Compared to the control experiment, which only assimilated conventional observations and radar data, the MWRPS experiment, which assimilated conventional observations, the ground-based microwave radiometer profiles and the radar data, had a positive impact on the forecasts of the RMAPS-ST. The results show that in comparison with the control test, the MWRPS experiment reproduced the heat island phenomenon in the observation better. The MWRPS assimilation reduced the bias and RMSE of two-meter temperature and two-meter specific humidity forecasting in the 0–12 h of the forecast range. Furthermore, assimilating the MWRPS improved both the distribution and the intensity of the hourly rainfall forecast, as compared with that of the control experiment, with observations that predicted the process of the precipitation enhancement in the urban area of Beijing.

Exploring the representation of clouds and humidity in the Arctic with cloud-resolving simulations using ICON-LEM

<p>The discussions around Arctic Amplification have led to extensive research, as done in the transregional collaboration (AC)³. One focus are the feedback mechanisms that are strengthening or weakening the warming. Several of these feedbacks involve moisture in the atmosphere in all phases. To understand these better we have been running and analysing daily cloud-resolving simulations. We performed these simulations for a region more strongly affected by the warming around Ny-Ålesund (Svalbard), which is challenging due to its diverse surface properties and mountainous surrounding. We have created an outstandingly large data set of several months of these simulations with 600 m resolution, using the Icosahedral non-hydrostatic model in the large-eddy mode (ICON-LEM).</p> <p>To gain some understanding of how well the model can represent such a complex location, we evaluated the performance of the model. For this, we used a range of observations from the measurement super-site located at Ny-Ålesund. This included radiosondes [1], a rain gauge, a microwave radiometer and further processed remote sensing data. Combining the measurements and simulations enables us to provide thorough statistics for different variables connected to clouds and to establish an understanding of how well they are represented.</p> <p>We show that the model is capable of simulating the two distinct flow regimes in the boundary layer and the free troposphere. Further, we found a tendency in the model to misrepresent liquid and mixed-phase clouds as purely ice clouds. Though the water vapour is well captured, we found further steps in the chain towards precipitation formation are insufficiently represented. Through the use of forward simulations and expanded model output, we can continue to get a better picture of possibilities to understand and improve the microphysical processes.</p> <p><em>This work was supported by the</em><em> DFG funded Transregio-project TR 172 “Arctic Amplification </em>(AC)3<em>“.</em></p> <p><strong>References</strong></p> <p>[1] M. Maturilli, High resolution radiosonde measurements from station Ny-Ålesund (2017-04 et seq). <em>Alfred</em> <em>Wegener Institute - Research Unit Potsdam, PANGAEA</em>, https://doi.org/10.1594/PANGAEA.914973 (2020)</p>

Disentangling the contributions of orographic waves, boundary-layer coupling, and aerosol to the occurrence of ice in mixed-phase clouds

<p>We will present a study on the impacts of orographic waves, surface coupling, and aerosol load on the frequency of heterogeneous ice formation in stratiform clouds using ground-based remote-sensing observations. Disentangling the convoluted effects of vertical motions and aerosols is critical for the understanding of heterogeneous ice formation and requires comprehensive observations. For the study, multi-year datasets from Punta Arenas (53.1°S 70.9°W, Chile, >2 years) and the northern hemispheric sites of Leipzig (51.4°N 12.4°E, Germany, 2.6 years) and Limassol (34.7°N 33.0°E, Cyprus, 1.5 years) were obtained by the same set of ground-based instruments (35-GHz cloud radar, Raman polarization lidar, 14-channel microwave radiometer, Doppler lidar, and disdrometer). The datasets at Limassol and Punta Arenas resemble the first multi-year ground-based remote-sensing datasets in the Eastern Mediterranean and in the western part of the Southern Ocean, respectively.</p> <p>The cloud properties were extracted from the synergistic dataset and the following key results on the efficiency of heterogeneous ice formation emerged:<br />The apparent lack of ice forming clouds at Punta Arenas below -15 <strong>°</strong>C can be related to orographic gravity waves, which allow persistent liquid saturation. These clouds could be identified by the autocorrelation function of the in-cloud vertical air velocity. Additionally, a correlation between the surface-coupling of a cloud and the likelihood of ice formation was found for Punta Arenas and Leipzig. At T>-10°C clouds coupled to the aerosol-rich boundary layer, were found to contain ice more frequently. Taking both effects into account, free-tropospheric, fully turbulent clouds at Punta Arenas form ice less frequently than their northern-hemispheric counterparts. This difference is linked to the lower abundance of INP in the free troposphere over the Southern Ocean.</p>

The Leipzig Aerosol and Cloud Remote Observations System LACROS – a mobile infrastructure for aerosol-cloud-interaction observations at hot spots of atmospheric research

<p>The large number of unsolved questions concerning the interaction between aerosol particles and clouds and corresponding indirect effects on precipitation and radiative transfer demand new measurement strategies and systems to resolve the atmospheric processes involved. Obtaining synergistic information about cloud and aerosol properties from multi–instrument and hence multi–sensor observations is a key approach to overcome the current lack of knowledge. Motivated by these needs, the mobile multi–instrument platform Leipzig Aerosol and Cloud Remote Observations System LACROS was set-up in 2011 by Leibniz Institute for Tropospheric Research (TROPOS). LACROS nowadays is the central component of a sophisticated framework of synergistic state-of-the-art measurement techniques and methodologies, embedded into an environment of comprehensive data management.</p> <p>The current setup of LACROS comprises a set of state-of-the-art remote-sensing instruments such as a 35-GHz scanning polarimetric cloud radar, multi-wavelength polarization Raman lidars, Doppler lidar, micro rain radar, microwave radiometer, laser disdrometer, as well as sensors for direct and diffuse downwelling solar and terrestrial radiation. All instruments are installed within customized sea-freight containers. This ensures a highest-possible mobility of the whole set of instruments. LACROS is a central mobile exploratory platform of the European Union Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS, http://www.actris.net). A variety of ways for physical, remote, and virtual access to the LACROS capabilities are provided via the European Union project ATMO-ACCESS (https://www.atmo-access.eu).</p> <p>LACROS measurements focus on three main tasks: (1) Investigation of mixed-phase cloud processes by exploiting co‐located remote-sensing observations of microphysical properties and radiative effects of aerosols and clouds and their interactions. (2) Instrument validation and development of algorithms and new measurement techniques for cloud and aerosol microphysics retrievals such as, i.e., dual‐field‐of‐view lidar to derive cloud droplet size information, or retrievals of aerosol microphysical properties from combined lidar and Sun photometer measurements. (3) Field deployments in key regions of atmospheric research, where the processes under investigation are already naturally constrained and observations can ideally be combined with in-situ observations or model simulations.</p> <p> </p> <p>This contribution will present the current setup of LACROS and its recent deployments in Leipzig, the Netherlands, Cyprus and southern Chile, results of aerosol-cloud-interaction studies by means of both, case studies and multi-site long-term statistics, as well as an overview on the current and future involvement of LACROS in cal/val activities of new methods and satellite missions. </p>

Calibration of water vapor Raman lidar measurements by use of radiosonde and microwave radiometer measurements

<p>Water vapor profiles with high vertical and temporal resolution were determined by use of the Raman lidar PollyXT within the MOSAiC campaign in the Arctic during the winter time 2019 – 2020. These measurements need a calibration. Usually, radiosonde data are utilized to calibrate the lidar data by the profile or the linear fit method, respectively. The radiosonde is drifting with the wind; thus, it is often measuring different atmospheric volumes compared to the lidar observations.</p> <p>The period 5-7 February 2020 is used to demonstrate the results. The correlation coefficient of the linear fit between the radiosonde and the lidar data varies with the different atmospheric conditions. The calibration results from the profile method coincide with those of the linear fit method, but the selection of the appropriate calibration setup is not straightforward. The varying correlation of the calibration results is attributed to the partly too low data-variability of the water vapor mixing ratio in the respective heights.  Moreover, the drift of the radiosondes with the wind and hence measurements of atmospheric volumes with lateral distances will have decreased the correlation between the lidar and the radiosonde measurements.</p> <p>During MOSAiC a microwave radiometer was collocated close to the lidar. This system was measuring the same atmospheric vertical column. Its product, the integrated water vapor, might be useful for the calibration of the lidar.</p> <p>Hence, the contribution will analyze the error of the lidar retrieved water vapor mixing ratio that includes the calibration with the radiosonde data and the microwave radiometer product.</p> <p> </p>

Cloud detection methods for a stand-alone ground based microwave radiometer

<p>The project “Pilotstation” at DWD employs a test bed setup to assess data availability, quality, observation impact and operational sustainability for five different ground based remote sensing instruments. The instruments in question, also referred to as “profilers”, are designed to continuously measure vertical profiles of thermodynamic and cloud/aerosol related variables.</p> <p>A ground based microwave radiometer (MWR) is one of the instruments evaluated in the project “Pilotstation”. MWR primarily measure downwelling radiation in the K-band and V-band in the form of brightness temperatures (TB). All-sky temperature and low-resolution humidity profiles as well as high-accuracy liquid water path (LWP, ΔLWP: ± 10-20 gm<sup>-2</sup>) and integrated water vapour (IWV, ΔIWV: ~ ± 0.5 kgm<sup>-2</sup>) are secondary products, which can be derived from the TB.</p> <p>The adaptation of the fast radiative transfer model RTTOV for ground based instruments enabled weather services to go forward with directly assimilating MWR TB rather than secondary products. First assimilation experiments of MWR TB at DWD were successful. Alongside other quality checks, the data assimilation (DA) relies on a cloud detection beforehand. The most frequent reason for rejecting data from DA is the suspected presence of clouds, consequently reliably identifying clouds without excessively rejecting clear-sky data is especially important for a high availability of suitable data.</p> <p>The study presented focuses on the requirements of operational DA and a stand-alone setup of an MWR. The work compares the performance of cloud detection algorithms used in scientific publications based on MWR observations. The comparisons include methods using TB, LWP and their variability. For this the CloudNet classification time series at Lindenberg and observation minus model background statistics serve as references. The presentation will also include progress made on refining the cloud detection schemes at hand in order to achieve a higher precision and to better meet the requirements of DA.</p>

Hydrometeor Classification of Winter Precipitation in Northern China Based on Multi-Platform Radar Observation System

Hydrometeor classification remains a challenge in winter precipitation cloud systems. To address this issue, 42 snowfall events were investigated based on a multi-platform radar observation system (i.e., X-band dual-polarization radar, Ka-band millimeter wave cloud radar, microwave radiometer, airborne equipment, etc.) in the mountainous region of northern China from 2016 to 2020. A fuzzy logic classification method is proposed to identify the particle phases, and the retrieval result was further verified with ground-based radar observation. Moreover, the hydrometeor characteristics were compared with the numerical simulations to clarify the reliability of the proposed hydrometeor classification approach. The results demonstrate that the X-/Ka- band radars are capable of identifying hydrometeor phases in winter precipitation in accordance with both ground observations and numerical simulations. Three particle categories, including snow, graupel and the mixture of snow and graupel are also detected in the winter precipitation cloud system, and there are three vertical layers identified from top to bottom, including the ice crystal layer, snow-graupel mixed layer and snowflake layer. Overall, this study has the potential for improving the understanding of microphysical processes such as freezing, deposition and aggregation of ice crystal particles in the winter precipitation cloud system.

The Performance of Commonly Used Surface-Based Instruments for Measuring Visibility, Cloud Ceiling, and Humidity at Cold Lake, Alberta

Data from automated meteorological instruments are used for model validation and aviation applications, but their measurement accuracy has not being adequately tested. In this study, a number of ground-based in-situ, remote-sensing instruments that measure visibility (VIS), cloud base height (CBH), and relative humidity (RH) were tested against data obtained using standard reference instruments and human observations at Cold Lake Airport, Alberta, Canada. The instruments included the Vaisala FS11P and PWD22 (FSPW), a profiling microwave radiometer (MWR), the Jenoptik ceilometer, Rotronic, Vaisala WXT520, AES-Dewcell RH, and temperature sensors. The results showed that the VIS measured using the FSPWs were well correlated with a correlation coefficient (R) of 0.84 under precipitation conditions and 0.96 during non-precipitating conditions (NPC), indicating very good agreement. However, the FS11P on average measured higher VIS, particularly under NPC. When the FSPWs were compared against human observation, a significant quantization in the data was observed, but less was noted during daytime compared to nighttime. Both probes measured higher VIS compared to human observation, and the calculated R was close to 0.6 for both probes. When the FSPWs were compared against human observation for VIS < 4 km, the calculated mean difference (MD) for the PWD22 (MD ≈ 0.98 km) was better than the FS11P (MD ≈ 1.37 km); thus, the PWD22 was slightly closer to human observation than the FS11P. No significant difference was found between daytime and nighttime measured VIS as compared to human observation; the instruments measured slightly higher VIS. Two extinction parameterizations as functions of snowfall rate were developed based on the VFPs measurements, and the results were similar. The Jenoptik ceilometer generally measured lower CBH than human observation, but the MWR measured larger CBHs for values <2 km, while CBHs were underestimated for higher CBHs.

Boundary-layer height and surface stability at SMEAR II, Hyytiälä, Finland in ERA5 and observations

The planetary boundary layer (BL) height and stratification are key parameters in determining the exchange of heat, momentum, moisture and trace gases between the surface and the free troposphere. Numerous different methods have been used to quantify the BL height and these methods have been applied to a wide variety of observational data sets obtained from different instruments and to numerical model output. We investigate the BL height at the Hyytiälä SMEAR II station in southern Finland diagnosed from radiosonde observations, a microwave radiometer (MWR) and ERA5 reanalysis. Four different algorithms are used to diagnose the BL height from the radiosondes. The diagnosed BL height is sensitive to the method used and the level of agreement, and the sign of systematic bias, between the 4 different methods depends on the surface-layer stability. For example, for very unstable situations, the median BL height diagnosed from the radiosondes varies from 600 m to 1500 m depending on which method is applied. Good agreement between the BL height in ERA5 and diagnosed from the radiosondes using Richardson number-based methods is found for almost all stability classes, suggesting that ERA5 has adequate vertical resolution near the surface to resolve the BL structure. However, ERA5 overestimates the BL height in very stable conditions highlighting the on-going challenge for numerical models to correctly resolve the stable BL. Furthermore, ERA5 BL height differs most from the radiosondes at 18 UTC suggesting ERA5 does not resolve the evening transition correctly. This study has also shown that BL height estimates from the MWR are reliable in unstable situations but often are inaccurate under stable conditions when, in comparison to ERA5 BL heights, they are much deeper. The errors in the MWR BL height estimates originate from the limitations of the manufacturers algorithm for stable conditions and also the mis-identification of the type of BL. A climatology of the annual and diurnal cycle of BL height and observed surface layer stability was created. The shallowest (353 m) monthly median BL height occurs in February and the deepest (576 m) in June. In winter there is no diurnal cycle in BL height, unstable BLs are rare yet so are very stable BLs. The shallowest BLs occur at night in spring and summer and very stable conditions are most common at night in the warm season. Finally, using ERA5 gridded data we determined that the BL height observed at Hyytiälä is representative of most land areas in southern and central Finland. However, the spatial variability of the BL height is largest during daytime in summer reducing the area over which BL height observations from Hyytiälä would be representative of.

A Digital-Simulation Model for a Full-Polarized Microwave Radiometer System and Its Calibration

A digital-correlation full-polarized microwave radiometer is an important passive remote sensor, as it can obtain the amplitude and phase information of an electromagnetic wave at the same time. It is widely used in the measurement of sea surface wind speed and direction. Its configuration is complicated, so the error analysis of the instrument is often difficult. This paper presents a full-polarized radiometer system model that can be used to analyze various errors, which include input signal models and a full-polarized radiometer (receiver) model. The input signal models are generated by WGN (white Gaussian noise), and the full-polarized radiometer model consists of an RF front-end model and digital back-end model. The calibration matrix is obtained by solving the overdetermined equations, and the output voltage is converted into Stokes brightness temperature through the calibration matrix. Then, we use the four Stokes parameters to analyze the sensitivity, linearity, and calibration residuals, from which the simulation model is validated. Finally, two examples of error analysis, including gain imbalance and quantization error, are given through a simulation model. In general, the simulation model proposed in this paper has good accuracy and can play an important role in the error analysis and pre-development of the fully polarized radiometer.