NCAR Research Articles

Hydrological Extremes in the Canadian Prairies in the Last Decade due to the ENSO Teleconnection—A Comparative Case Study Using WRF

In the Prairie provinces of Alberta, Saskatchewan, and Manitoba, agricultural production depends on winter and spring precipitation. There is large interannual variability related to the teleconnection between the regional hydroclimate and El Niño and La Niña in the Tropical Pacific. A modeling experiment was conducted to simulate climatic and hydrological parameters in the Canadian Prairie region during strong El Niño and La Niña events of the last decade in 2015–2016 and 2010–2011, respectively. The National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model was employed to perform two sets of sensitivity experiments with a nested domain at 10 km resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA) interim data as the lateral boundary forcing. Analysis of the hourly model output provides a detailed simulation of the drier winter, with less soil moisture in the following spring, during the 2015–2016 El Niño and a wet winter during the La Niña of 2010–2011. The high-resolution WRF simulation of these recent weather events agrees well with observations from weather stations and water gauges. Therefore, we were able to take advantage of the WRF model to simulate recent weather with high spatial and temporal resolution and thus study the changes in hydrometeorological parameters across the Prairie during the two extreme hydrological events of the last decade.

Optimizing high-resolution Community Earth System Model on a heterogeneous many-core supercomputing platform

Abstract. With semiconductor technology gradually approaching its physical and thermal limits, recent supercomputers have adopted major architectural changes to continue increasing the performance through more power-efficient heterogeneous many-core systems. Examples include Sunway TaihuLight that has four management processing elements (MPEs) and 256 computing processing elements (CPEs) inside one processor and Summit that has two central processing units (CPUs) and six graphics processing units (GPUs) inside one node. Meanwhile, current high-resolution Earth system models that desperately require more computing power generally consist of millions of lines of legacy code developed for traditional homogeneous multicore processors and cannot automatically benefit from the advancement of supercomputer hardware. As a result, refactoring and optimizing the legacy models for new architectures become key challenges along the road of taking advantage of greener and faster supercomputers, providing better support for the global climate research community and contributing to the long-lasting societal task of addressing long-term climate change. This article reports the efforts of a large group in the International Laboratory for High-Resolution Earth System Prediction (iHESP) that was established by the cooperation of Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM), Texas A&M University (TAMU), and the National Center for Atmospheric Research (NCAR), with the goal of enabling highly efficient simulations of the high-resolution (25 km atmosphere and 10 km ocean) Community Earth System Model (CESM-HR) on Sunway TaihuLight. The refactoring and optimizing efforts have improved the simulation speed of CESM-HR from 1 SYPD (simulation years per day) to 3.4 SYPD (with output disabled) and supported several hundred years of pre-industrial control simulations. With further strategies on deeper refactoring and optimizing for remaining computing hotspots, as well as redesigning architecture-oriented algorithms, we expect an equivalent or even better efficiency to be gained on the new platform than traditional homogeneous CPU platforms. The refactoring and optimizing processes detailed in this paper on the Sunway system should have implications for similar efforts on other heterogeneous many-core systems such as GPU-based high-performance computing (HPC) systems.

Predicting Vertical Concentration Profiles in the Marine Atmospheric Boundary Layer With a Markov Chain Random Walk Model.

In an effort to better represent aerosol transport in mesoscale and global-scale models, large eddy simulations (LES) from the National Center for Atmospheric Research (NCAR) Turbulence with Particles (NTLP) code are used to develop a Markov chain random walk model that predicts aerosol particle profiles in a cloud-free marine atmospheric boundary layer (MABL). The evolution of vertical concentration profiles are simulated for a range of aerosol particle sizes and in a neutral and an unstable boundary layer. For the neutral boundary layer we find, based on the LES statistics and a specific model time step, that there exist significant correlation for particle positions, meaning that particles near the bottom of the boundary are more likely to remain near the bottom of the boundary layer than being abruptly transported to the top, and vice versa. For the unstable boundary layer, a similar time interval exhibits a weaker tendency for an aerosol particle to remain close to its current location compared to the neutral case due to the strong nonlocal convective motions. In the limit of a large time interval, particles have been mixed throughout the MABL and virtually no temporal correlation exists. We leverage this information to parameterize a Markov chain random walk model that accurately predicts the evolution of vertical concentration profiles. The new methodology has significant potential to be applied at the subgrid level for coarser-scale weather and climate models, the utility of which is shown by comparison to airborne field data and global aerosol models.

The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA)

ABSTRACTTo explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.

Connection between the South and East Asian Monsoons: Comparing Summer Monsoon Rainfall of Pakistan and South Korea

This study investigates the tele-connection of the southeast Asian monsoon systems by comparing the summer monsoon (June to September) rainfall variability between Pakistan and south Korea. The daily data sets (19812014) of rainfall of Pakistan and south Korea are utilized to explore the possible link. The data products of the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) were also used for the understanding of the large-scale atmospheric environments. The patterns of summer monsoon rainfall on a daily basis between Pakistan and south Korea followed to each other throughout the year. Sub-seasonal differences of the summer monsoon revealed that July is the wettest month in both countries. The large-scale atmospheric environment of higher geopotential height revealed that the Tibetan high and the western north Pacific subtropical high are showing positive anomalies during positive phases over south Asia and east Asia, respectively. The anomalies of zonal wind are negative during positive phase and adverse in the negative phase between 20-40oN. The reduced westerly is interpreted as the seasonal variation and moving of jet streams from the east Asian route. The Tibetan high, northwestern Pacific subtropical high and the east Asian jet stream have reliable and sufficient linkage between the Pakistan and south Korea summer monsoon system.

Atmospheric Circulation as a Factor Contributing to Increasing Drought Severity in Central Europe

AbstractLong‐lasting and severe droughts seriously threaten agriculture, ecosystems, and society. Summer 2018 in central Europe was characterized by unusually persistent heat and drought, causing substantial economic losses, and became a part of a several years long dry period observed across this region. This study assesses the magnitude of the recent drought within a long‐term context and links the increased drought severity to changes in atmospheric circulation. Temporal variability of drought conditions since the late 19th century was analyzed at seven long‐term stations distributed across the Czech Republic using the Palmer Drought Severity Index and the Standardized Precipitation Evaporation Index. The Palmer Z Index and a variation of the Standardized Precipitation Evaporation Index were used to study rapidly emerging short‐term droughts and to link these episodes to atmospheric circulation. Changes in circulation were analyzed through circulation types calculated from flow strength, direction and vorticity in mean sea level pressure data from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis for 1948–2018. Increasing drought severity across the Czech Republic with record‐low values of the drought indices during 2015–2018 was found. The trend was distinctive in both vegetation (April–September) and cold (October–March) periods. The tendency toward more severe droughts in recent decades was linked to changes in frequency of dry and wet circulation types, highlighting the important role of atmospheric circulation in regional climate. It remains an open question whether the significantly increasing frequency of dry circulation types in the vegetation period is related to climate change, or rather represents multidecadal climate variability.

Synoptic characteristics and ozone distributions associated with spring dust classes over the northern Arabian Peninsula

The spring season can be classified into five dust classes over the northern Arabian Peninsula (AP), and they range from free (represented 3.3% of measured period) to strong (represented 21.4% of measured period) and were categorized based on their Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI) values. The horizontal distribution of total ozone and the synoptic characteristics of these classes were analyzed using the TOMS total ozone and National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis meteorological data. The horizontal distribution of total ozone for the dust classes indicated that ozone increased (from 260 DU for dust-free to 280 DU for strong dust classes) over the southern study region. Meanwhile, the amount (from 380 DU for the dust-free to 360 DU for strong dust classes) and gradient of ozone decreased over the subtropical region as the dust class intensified, and the concentration of ozone toward the southern region was related to weak dust classes. The synoptic study indicated that the dust systems over the northern AP intensified when the atmospheric conditions were favorable for deepening the surface low pressure or a trough influenced the area and an upper-level pronounced trough and ridge occurred over the eastern and western AP. An examination of the dust cases indicated that the synoptic features that occurred with dust over the northern AP did not occur via the atmospheric systems themselves but rather via the relative relationships between these systems. Furthermore, the results confirmed that ozone over the northern (southern) AP decreased (increased) as the dust features strengthened (weakened).

A modeling examination of cloud seeding conditions under the warmer climate in Utah, USA

Assessment of how the wintertime cloud seeding conditions over Utah's mountains may change provides essential information for the state to work on increasing its snowpack in the warmer climate. In this study, data from the National Center for Atmospheric Research's high-resolution, convection-permitting Weather Research and Forecasting model are used to evaluate suitable conditions for wintertime cloud seeding over Utah's mountains, and to estimate future changes to these conditions under a pseudo-global warming scenario. While previous studies have examined historical cloud seeding conditions, this is the first study that explores the impact of future climate change on these conditions. Based on two atmospheric variables commonly used in cloud-seeding operations, i.e. average temperature from the surface to 1 km above ground level (−20 °C≤T≤−6 °C) and vertically integrated supercooled liquid water (SLW > 0.01 mm), Utah's mountains are suitable for seeding more than 20% of the time during winter. A greater rate of suitability exists in the northern and Uinta Mountains. In the warmer climate under a high-emissions scenario, these seeding conditions may wane as the percentage of precipitating clouds suitable for seeding, about 60% under current conditions, would decline to about 40% across the state with significance. This projected decrease is due to rising temperatures and a decreased frequency of precipitation events. These findings imply that climate warming will narrow the window of opportunity for winter cloud seeding operation in Utah.

Cyclone tracking affecting precipitation in Iran

In recent years, fluctuations in precipitation have caused many environmental problems in Iran; understanding the reasons of these changes is essential for sustainable development planning. Mid-latitude cyclones play a significant role in providing precipitation over Iran. In the current research, cyclone centers were defined based on two features; on the investigation point, the geopotential height is minimum compared with the eight neighbors and the weighted average of the geopotential heights on the investigation point and the eight neighbors is at least 100 geopotential meter per 1000 km. Geopotential heights and the sea level pressure data during the period of 1986–2013 for spatial dimensions, 10 to 60 degrees of north latitude and − 40 to 80 degrees of east longitude, were taken from the American National Center for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR). Data review revealed that there is a northward shift in cyclone routes associated with precipitation for the country. Moreover, the cyclones entering from the eastern Mediterranean have been reduced. Global warming and changes in the path of storm tracks can be regarded as possible reasons behind the observed changes in precipitation in Iran. In response to the shifts in cyclonic routes entering Iran, changes in precipitation have also occurred. These reductions in precipitation may continue, at least, over foreseeable future.

Taiwan Earth System Model Version 1: description and evaluation of mean state

Abstract. The Taiwan Earth System Model (TaiESM) version 1 is developed based on Community Earth System Model version 1.2.2 of National Center for Atmospheric Research. Several innovative physical and chemical parameterizations, including trigger functions for deep convection, cloud macrophysics, aerosol, and three-dimensional radiation–topography interaction, as well as a one-dimensional mixed-layer model optional for the atmosphere component, are incorporated. The precipitation variability, such as diurnal cycle and propagation of convection systems, is improved in TaiESM. TaiESM demonstrates good model stability in the 500-year preindustrial simulation in terms of the net flux at the top of the model, surface temperatures, and sea ice concentration. In the historical simulation, although the warming before 1935 is weak, TaiESM captures the increasing trend of temperature after 1950 well. The current climatology of TaiESM during 1979–2005 is evaluated by observational and reanalysis datasets. Cloud amounts are too large in TaiESM, but their cloud forcing is only slightly weaker than observational data. The mean bias of the sea surface temperature is almost 0, whereas the surface air temperatures over land and sea ice regions exhibit cold biases. The overall performance of TaiESM is above average among models in Coupled Model Intercomparison Project phase 5, particularly in that the bias of precipitation is smallest. However, several common discrepancies shared by most models still exist, such as the double Intertropical Convergence Zone bias in precipitation and warm bias over the Southern Ocean.

광양만권 오존농도 특성 조사 (순천지역을 중심으로)

This study investigates distribution of highly concentrated ozone generation for the last 5 years(2015~2019). To analyze the air flow and weather relation when highly concentrated ozone generated, the air current pattern was analyzed by using HYSPLIT model of National Center for Atmospheric Research, US. After HYSPLIT analysis, the characteristics of Case 1 ~ 4 were analyzed according to the travel distance. Case 3, 4 showed the most influence on generating highly concentrated ozone; it showed short travel distance. Also for Suncheon in Gwangyang-Bay area, the cause of considerable frequency and lasting time of YH and SD monitoring sites was analyzed. The total accumulated time of high ozone concentration 242 hours at YH site and 179 hours at SD site. The characteristics of the monitoring sites were analyzed by analyzing the correlation among ozone concentration, meteorological data and air pollutants. YH monitoring site showed the lowest wind speed and high correlation of Insolation. SD monitoring site showed considerably strong correlation of fine dust and ultrafine dust. Also the locations of the two sites are in leeward of the industrial complex area, therefore ozone can easily be generated and the wind speed is low, which can cause atmospheric congestion and generate highly concentrated ozone for a long time.

An Evaluation of the Large‐Scale Atmospheric Circulation and Its Variability in CESM2 and Other CMIP Models

AbstractThe Community Earth System Model 2 (CESM2) is the latest Earth System Model developed by the National Center for Atmospheric Research in collaboration with the university community and is significantly advanced in most components compared to its predecessor (CESM1). Here, CESM2's representation of the large‐scale atmospheric circulation and its variability is assessed. Further context is providedthrough comparison to the CESM1 large ensemble and other models from the Coupled Model Intercomparison Project (CMIP5 and CMIP6). This includes an assessment of the representation of jet streams and storm tracks, stationary waves, the global divergent circulation, the annular modes, the North Atlantic Oscillation, and blocking. Compared to CESM1, CESM2 is substantially improved in the representation of the storm tracks, Northern Hemisphere (NH) stationary waves, NH winter blocking and the global divergent circulation. It ranks within the top 10% of CMIP class models in many of these features. Some features of the Southern Hemisphere (SH) circulation have degraded, such as the SH jet strength, stationary waves, and blocking, although the SH jet stream is placed at approximately the correct location. This analysis also highlights systematic deficiencies in these features across the new CMIP6 archive, such as the continued tendency for the SH jet stream to be placed too far equatorward, the North Atlantic westerlies to be too strong over Europe, the storm tracks as measured by low‐level meridional wind variance to be too weak and a lack of blocking in the North Atlantic sector.

The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting

One way to mitigate the variability of wind and solar power generation is to install the corresponding plants in nearby locations. For example, in Kuwait, the facility at Shagaya Renewable Energy Park is located in a desert area with both photovoltaic panels and wind turbines that allow the continuous generation of renewable energy throughout the day. The National Center for Atmospheric Research (NCAR) has developed a system to generate probabilistic wind and solar predictions for the Shagaya facility. These predictions are based on the analog ensemble technique that post-processes the wind speed and solar irradiance predictions based on a combination of multiple models including the Weather Research and Forecasting (WRF) numerical model. The ensemble forecasts have 20 members and are generated independently at each wind and solar power production facility. Here we present a method based on the Schaake Shuffle (SS) technique to pair the ensemble members from the independent systems to obtain a unique ensemble prediction of the aggregated wind and solar generation. After reordering through the SS technique, the corresponding paired solar and wind power members can be summed to build a unique ensemble of combined generation that is statistically consistent, as verified by the presented metrics.

Assessing the performance of climate change simulation results from BESM-OA2.5 compared with a CMIP5 model ensemble

Abstract. The main features of climate change patterns, as simulated by the coupled ocean–atmosphere version 2.5 of the Brazilian Earth System Model (BESM), are compared with those of 25 other CMIP5 models, focusing on temperature, precipitation, atmospheric circulation, and radiative feedbacks. The climate sensitivity to quadrupling the atmospheric CO2 concentration was investigated via two methods: linear regression (Gregory et al., 2004) and radiative kernels (Soden and Held, 2006; Soden et al., 2008). Radiative kernels from both the National Center for Atmospheric Research (NCAR) and the Geophysical Fluid Dynamics Laboratory (GFDL) were used to decompose the climate feedback responses of the CMIP5 models and BESM into different processes. By applying the linear regression method for equilibrium climate sensitivity (ECS) estimation, we obtained a BESM value close to the ensemble mean value. This study reveals that the BESM simulations yield zonally average feedbacks, as estimated from radiative kernels, that lie within the ensemble standard deviation. Exceptions were found in the high latitudes of the Northern Hemisphere and over the ocean near Antarctica, where BESM showed values for lapse rate, humidity feedback, and albedo that were marginally outside the standard deviation of the values from the CMIP5 multi-model ensemble. For those areas, BESM also featured a strong positive cloud feedback that appeared as an outlier compared with all analyzed models. However, BESM showed physically consistent changes in the temperature, precipitation, and atmospheric circulation patterns relative to the CMIP5 ensemble mean.

Planetary Wave (PW) Generation in the Thermosphere Driven by the PW‐Modulated Tidal Spectrum

AbstractThe National Center for Atmospheric Research thermosphere‐ionosphere‐electrodynamics general circulation model (TIE‐GCM) is used to conduct numerical experiments that isolate and elucidate a substantial modification of the quasi‐6‐day wave (Q6DW) above 110 km due to presence of the planetary wave (PW)‐modulated tidal spectrum. A two‐stage nonlinear tidal interaction is proposed, and its role in vertical coupling by the Q6DW is quantified. The theory enables calculation of net Q6DW accelerations and heating rates in the height‐latitude domain (90–300 km; ±75°) due to wave‐wave interactions of up to 10 ms−1 day−1 and 8 K day−1, respectively. The Global‐Scale Wave Model (GSMW) is used to demonstrate that these forcings produce Q6DW zonal and meridional wind amplitudes, and temperatures, of order 5–20 ms−1, 5–15 ms−1, and 5–10 K, respectively. Notably, Q6DW thermal forcing in the GSWM accounts for near‐doubling of the wind magnitudes calculated with momentum forcing alone. The computed values are comparable to the 10–22 ms−1, 3–12 ms−1, and 4–12 KQ6DW amplitudes calculated with lower‐boundary forcing of the Q6DW also included. The proposed two‐stage interaction plausibly impacts other PW wind structures in the dynamo region and thus how PW in general participate in atmosphere‐ionosphere coupling.

ASLO 2020 Award Winners

<scp>ASLO</scp> 2020 Award Winners

Investigation into sustainable water use in India using combined large-scale earth system-based modelling and census-based statistical data

Sustainable water use and water scarcity are major concerns in developing countries such as India. Rapidly growing population along with increasing economic and technological development in India have resulted in increased water use leading to severe water scarcity. The present study aims to quantify and assess sustainable water use and water scarcity in India. A data-intensive approach is employed at a State spatial resolution and monthly temporal scales during the period 1991–1999. The Water Stress Index (WSI), which is defined as the ratio of total water use to available water, is used as a measure of blue water scarcity. The total available water includes surface water and groundwater while the total water uses include irrigation, industrial, domestic and environmental uses. A Community Land Model (CLM 4.0) developed by the National Centre for Atmospheric Research (NCAR), is used to quantify the total available water in India. Water uses in India are reconstructed using a census-based statistical database while the environmental water demand is modelled following the hydrology-based approach, which allocates environmental flows as a proportion of the available water, with seasonally defined environmental flow thresholds. Further, the excess water component, defined as the amount of water remaining after meeting the demands at each time step, is incorporated into the modelling framework by adding it to the available water pool. The study estimates that 60% of the population in India faces severe water stress during the analysis period, and is more predominant in the States of Rajasthan, Gujarat, Uttar Pradesh and Maharashtra. Moreover, non-renewable groundwater abstraction, which is defined as the amount of groundwater abstraction more than the groundwater recharge, is also quantified. This study identifies severe groundwater depletion in the north-western parts of India, consistent with the current estimates from GRACE satellite observations.

A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

Monitoring the differential reflectivity and receiver calibration of the German polarimetric weather radar network

Abstract. It is a challenge to calibrate differential reflectivity ZDR to within 0.1–0.2 dB uncertainty for dual-polarization weather radars that operate 24∕7 throughout the year. During operations, a temperature sensitivity of ZDR larger than 0.2 dB over a temperature range of 10 ∘C has been noted. In order to understand the source of the observed ZDR temperature sensitivity, over 2000 dedicated solar box scans, two-dimensional scans of 5∘ azimuth by 8∘ elevation that encompass the solar disk, were made in 2018 from which horizontal (H) and vertical (V) pseudo antenna patterns are calculated. This assessment is carried out using data from the Hohenpeißenberg research radar which is identical to the 17 operational radar systems of the German Meteorological Service (Deutscher Wetterdienst, DWD). ZDR antenna patterns are calculated from the H and V patterns which reveal that the ZDR bias is temperature dependent, changing about 0.2 dB over a 12 ∘C temperature range. One-point-calibration results, where a test signal is injected into the antenna cross-guide coupler outside the receiver box or into the low-noise amplifiers (LNAs), reveal only a very weak differential temperature sensitivity (&lt;0.02 dB) of the receiver electronics. Thus, the observed temperature sensitivity is attributed to the antenna assembly. This is in agreement with the NCAR (National Center for Atmospheric Research) S-Pol (S-band polarimetric radar) system, where the primary ZDR temperature sensitivity is also related to the antenna assembly (Hubbert, 2017). Solar power measurements from a Canadian calibration observatory are used to compute the antenna gain and to validate the results with the operational DWD monitoring results. The derived gain values agree very well with the gain estimate of the antenna manufacturer. The antenna gain shows a quasi-linear dependence on temperature with different slopes for the H and V channels. There is a 0.6 dB decrease in gain for a 10 ∘C temperature increase, which directly relates to a bias in the radar reflectivity factor Z which has not been not accounted for previously. The operational methods used to monitor and calibrate ZDR for the polarimetric DWD C-band weather radar network are discussed. The prime sources for calibrating and monitoring ZDR are birdbath scans, which are executed every 5 min, and the analysis of solar spikes that occur during operational scanning. Using an automated ZDR calibration procedure on a diurnal timescale, we are able to keep ZDR bias within the target uncertainty of ±0.1 dB. This is demonstrated for data from the DWD radar network comprising over 87 years of cumulative dual-polarization radar operations.