Representation of Rossby wave propagation and its effect on the teleconnection between the Indian summer monsoon and extratropical rainfall in the Met Office Unified Model

Compared with Global Atmosphere 6 (GA6) of the UK Met Office Unified Model (UM), the dry bias over the Indian monsoon region in Global Atmosphere 7 (GA7) is significantly reduced. However, the physical processes controlling how this reduced dry bias in India influences rainfall teleconnections in the extratropics remain unclear. Thus, in this study, we use Rossby wave tracing in a horizontally nonuniform background flow to investigate how the improved simulation of monsoon rainfall in GA7 compared with GA6 affects extratropical rainfall teleconnections. We find that wave rays emanating from the upper troposphere in the Indian monsoon region first propagate westward, then divide into the Northern Hemisphere (NH) subtropical westerlies over Asia and the Southern Hemisphere (SH) subtropical westerlies. The wave ray trajectories in GA7 in years of strong Indian summer monsoon rainfall (ISMR) are closer to observations than those in GA6. We also find that the upper tropospheric meridional winds over the South Asian monsoon region and western Tibetan Plateau are much better simulated in GA7 than in GA6 owning to the improvement of ISMR and South Asian High (SAH), which leads to a more realistic simulation of the wave rays in GA7. The better simulated circulation teleconnections in GA7 then modulate the vertical motion and moisture transport, and hence affect extratropical rainfall anomalies in the NH and SH. This paper provides new insights for the assessment of tropical–extratropical teleconnections in models.

Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC⁴A

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly 50 stations distributed over the Caribbean arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality integrated water vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the trade winds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD)-to-IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams International Airport (GAIA), Bridgetown, Barbados. A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2), where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a collocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth-generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS-derived IWV peaks. Two successive peaks are observed on 22 January and 23–24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organization for five representative GNSS stations across the Caribbean arc using visible satellite images. A statistically significant link was found between the cloud patterns and the local IWV observations from the GNSS sites as well as the larger-scale IWV patterns from the ECMWF ERA5 reanalysis. The reprocessed ZTD and IWV data sets from 49 GNSS stations used in this study are available from the French data and service centre for atmosphere (AERIS) (https://doi.org/10.25326/79; Bock, 2020b).

A new approach for seasonal prediction using the coupled model CFSv2 with special emphasis onIndian Summer Monsoon Rainfall

<p>Predicting the Indian summer monsoon (ISM) is a challenging task due to the complexity of the climate system. Any improvement in the prediction skill of ISM in general circulation models would highly benefit the country as a whole due to its close linkage with the economy. In this study, we have adopted a new strategy to improve the ISM rainfall (ISMR) bias and prediction using the National Centers for Environmental Prediction-Climate Forecast System version 2 (NCEP-CFSv2). This model is currently used for the seasonal prediction in many countries including India but is known to have persistent dry bias over the Indian landmass. Three sets of hindcast experiments are carried out for 9 months each, for the period 2005-2019. The experiments differ from each other in the way they are initialized. Significant reduction in dry bias over the Indian landmass in the summer season with improved representation of tropical Indo Pacific sea surface temperature is reported from the new initialization  strategy. It is found that enhanced moisture transport to Indian landmass from the Arabian Sea,  improved representation of mean cyclonic circulation over north India, weak southeasterlies from Bay of Bengal and western Pacific together with enhanced Walker circulation contributed to the reduction  in dry bias over the Indian landmass. In addition to the above, the midlatitude circulation contribution by enhancing the strength of Subtropical High in the North Pacific resulted enhanced precipitation over the Indian landmass. The initialization strategy used here would be highly useful for improving the seasonal monsoon forecast.</p>

IWV observations from a network of ground-based GNSS receivers during EUREC4A

<p>IWV data were retrieved from a network of nearly fifty Global Navigation Satellite System (GNSS) stations distributed over the Caribbean arc for the period 1 January-29 February 2020 encompassing the EUREC4A field campaign. Two of the stations had been installed at the Barbados Cloud Observatory (BCO) during fall 2019 in the framework of the project and are still running. All other stations are permanent stations operated routinely from various geodetic and geophysical organisations in the region. High spatial and temporal Integrated Water Vapour (IWV) observations will be used to investigate the atmospheric environment during the life cycle of convection and its feedback on the large-scale circulation and energy budget.</p><p>This paper describes the ground-based GNSS data processing details and assesses the quality of the GNSS IWV retrievals as well as the IWV estimates from radiosoundings, microwave radiometer measurements and ERA5 reanalysis.</p><p>The GNSS results from five different processing streams run by IGN and ENSTA-B/IPGP are first intercompared. Four of the streams were run operationally, among one was in near-real time, and one was run after the campaign in a reprocessing mode. The uncertainties associated with each of the data sets, including the zenith tropospheric delay to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the BCO and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (-2.9 kg/m2) and the GNSS results (-1.2 kg/m2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg/m2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The HATPRO IWV estimates agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to GNSS and BCO sondes before that date. ERA5 IWV estimates are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated.</p>

IWV observations in the Caribbean Arc from a network of ground-based GNSS receivers during EUREC⁴A

Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)).

Accuracy of Vaisala RS41 and RS92 Upper Tropospheric Humidity Compared to Satellite Hyperspectral Infrared Measurements

Radiosondes are important for calibrating satellite sensors and assessing sounding retrievals. Vaisala RS41 radiosondes have mostly replaced RS92 in the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) and the conventional network. This study assesses RS41 and RS92 upper tropospheric humidity (UTH) accuracy by comparing with Infrared Atmospheric Sounding Interferometer (IASI) upper tropospheric water vapor absorption spectrum measurements. Using single RS41 and RS92 soundings at three GRUAN and DOE Atmospheric Radiation Measurement (ARM) sites and dual RS92/RS41 launches at three additional GRUAN sites, collocated with cloud-free IASI radiances (OBS), we compute Line-by-Line Radiative Transfer Model radiances for radiosonde profiles (CAL). We analyze OBS-CAL differences from 2015 to 2020, for daytime, nighttime, and dusk/dawn separately if data is available, for standard (STD) RS92 and RS41 processing, and RS92 GRUAN Data Processing (GDP; RS41 GDP is in development). We find that daytime RS41 (even without GDP) has ~1% smaller UTH errors than GDP RS92. RS41 may still have a dry bias of 1–1.5% for both daytime and nighttime, and a similar error for nighttime RS92 GDP, while standard RS92 may have a dry bias of 3–4%. These sonde humidity biases are probably upper limits since “cloud-free” scenes could still be cloud contaminated. Radiances computed from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses match better than radiosondes with IASI measurements, perhaps because ECMWF assimilates IASI measurements. Relative differences between RS41 STD and RS92 GDP, or between radiosondes and ECMWF humidity profiles obtained from the radiance analysis, are consistent with their differences obtained directly from the RH measurements.

A lightweight balloon-borne mid-infrared hygrometer to probe the middle atmosphere: Pico-Light H₂O. Comparison with Aura-MLS v4 and v5 satellite measurements

Newly developed mid-infrared lightweight hygrometer, Pico-Light H2O has been tested in-flight on February 19, 2019 and October 16, 2019. It has been flown under a 1200 g rubber balloon operated by CNES from the Aire-sur-l'Adour facility (France) within the E.U. funded HEMERA WP11. During these two flights, we were able to obtain coincident MLS v4 and v5 water vapor and temperature profiles, leading to an inter-comparison between Pico-Light and Aura-MLS water vapor and temperature retrievals. Results from the comparison are in line with previous reported studies . Here, differences in the mid-latitude stratosphere and upper troposphere (20–316 hPa) are within 7 % and 64 % respectively. Largest differences with MLS v4 occurring within the upper troposphere nearby the cold point tropopause. The v5 MLS data have been corrected for observed dry bias nearby the tropopause, allowing to partially solve the observed discrepancies. Additionally, on February 19, the hygrometer has flown within an air filament from polar latitudes most of the flight for which a signature is observed on the water vapor profile and confirmed with ozone reanalysis from ERA 5and potential vorticity from MIMOSA advection model.

Potential Impacts of Assimilating All-Sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection

Geostationary infrared satellite observations are spatially dense [>1/(20 km)2] and temporally frequent (>1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

Sensitivity of Summer Precipitation over Korea to Convective Parameterizations in the RegCM4: An Updated Assessment

This study investigates the performance of the latest version of RegCM4 in simulating summer precipitation over South Korea, comparing nine sensitivity experiments with different combinations of convective parameterization schemes (CPSs) between land and ocean. In addition to the gross pattern of seasonal and monthly mean precipitation, the northward propagation of the intense precipitation band and statistics from extreme daily precipitation are thoroughly evaluated against gridded and in situ station observations. The comparative analysis of 10-year simulations demonstrates that no CPS shows superiority in both quantitative and qualitative aspects. Furthermore, a nontrivial discrepancy among the different observation datasets makes a robust assessment of model performance difficult. Regardless of the CPS over the ocean, the simulations with the Kain–Fritsch scheme over land show a severe dry bias, whereas the simulations with the Tiedtke scheme over land suffer from a limited accuracy in reproducing spatial distributions due to the excessive orographic precipitation. In general, the simulations with the Emanuel scheme over land are better at capturing the major characteristics of summer precipitation over South Korea, despite not all statistical metrics showing the best performance. When applying the Emanuel scheme to both land and the ocean, precipitation tends to be slightly overestimated. This deficiency can be alleviated by using either the Tiedtke or Kain–Fritsch schemes over the ocean instead. As few studies have applied and evaluated the Tiedtke and Kain–Fritsch schemes to the Korean region within the RegCM framework, and this study introduces the potential of these new CPSs compared with the more frequently selected Emanuel scheme, which is particularly beneficial to RegCM users.