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Systematic Differences in Aircraft and Radiosonde Temperatures

Implications for NWP and Climate Studies

Bradley A. Ballish and V. Krishna Kumar

Automated aircraft data are very important as input to numerical weather prediction (NWP) models because of their accuracy, large quantity, and extensive and different data coverage compared to radiosonde data. On average, aircraft mean temperature observation increments [MTOI; defined here as the observations minus the corresponding 6-h forecast (background)] are more positive (warmer) than radiosondes, especially around jet level. Temperatures from different model types of aircraft exhibit a large variance in MTOI that vary with both pressure and the phase of flight (POF), confirmed by collocation studies. This paper compares temperatures of aircraft and radiosondes by collocation and MTOI differences, along with discussing the pros and cons of each method, with neither providing an absolute truth.

Arguments are presented for estimating bias corrections of aircraft temperatures before input into NWP models based on the difference of their MTOI and that of radiosondes, which tends to cancel systematic errors in the background while using the radiosondes as truth. These corrections are just estimates because radiosonde temperatures have uncertainty and the NCEP background has systematic errors, in particular an MTOI of almost 2°C at the tropopause that is attributable in part to vertical interpolation errors, which can be reduced by increasing model vertical resolution. The estimated temperature bias corrections are predominantly negative, of the order of 0.5°–1.0°C, with relatively small monthly changes, and often have vertically deep amplitudes.

This study raises important issues pertaining to the NWP, aviation, and climate communities. Further metadata from the aviation community, field experiments comparing temperature measurements, and input from other NWP centers are recommended for refining bias corrections.

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Jordan C. Alpert and V. Krishna Kumar

Abstract

The spatial and temporal densities of Weather Surveillance Radar-1988 Doppler (WSR-88D) raw radar radial wind represent a rich source of high-resolution observations for initializing numerical weather prediction models. A characteristic of these observations is the presence of a significant degree of redundant information imposing a burden on an operational assimilation system. Potential improvement in data assimilation efficiency can be achieved by constructing averages, called super-obs. In the past, transmission of the radar radial wind from each radar site to a central site was confined to data feeds that filter the resolution and degrade the precision. At the central site, super-obs were constructed from this data feed and called level-3 super-obs. However, the precision and information content of the radial wind can be improved if data at each radar site are directly utilized at the highest resolution and precision found at the WSR-88D radar and then transmitted to a central site for processing in assimilation systems. In addition, with data compression from using super-obs, the volume of data is reduced, allowing quality control information to be included in the data transmission. The super-ob product from each WSR-88D radar site is called level-2.5 super-obs. Parallel, operational runs and case studies of the impact of the level-2.5 radar radial wind super-ob on the NCEP operational 12-km Eta Data Assimilation System (EDAS) and forecast system are compared with Next-Generation Weather Radar level-3 radial wind super-obs, which are spatially filtered and delivered at reduced precision. From the cases studied, it is shown that the level-3 super-obs make little or no impact on the Eta data analysis and subsequent forecasts. The assimilation of the level-2.5 super-ob product in the EDAS and forecast system shows improved precipitation threat scores as well as reduction in RMS and bias height errors, particularly in the upper troposphere. In the few cases studied, the predicted mesoscale precipitation patterns benefit from the level-2.5 super-obs, and more so when greater weight is given to these high-resolution/precision observations. Direct transmission of raw (designated as level 2) radar data to a central site and its use are now imminent, but this study shows that the level-2.5 super-ob product can be used as an operational benchmark to compare with new quality control and assimilation schemes.

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Sid-Ahmed Boukabara, Kevin Garrett, and V. Krishna Kumar

Abstract

The current constellation of environmental satellites is at risk of degrading due to several factors. This includes the following: 1) loss of secondary polar-orbiting satellites due to reaching their nominal lifetimes, 2) decrease in the density of extratropical radio-occultation (RO) observations due to a likely delayed launch of the Constellation Observing System for Meteorology, Ionosphere and Climate-2 (COSMIC-2) high inclination orbit constellation, and 3) the risk of losing afternoon polar-orbiting satellite coverage due to potential launch delays in the Joint Polar Satellite System (JPSS) programs. In this study, the impacts from these scenarios on the National Oceanic and Atmospheric Administration (NOAA) Global Forecast System skill are quantified. Performances for several metrics are assessed, but to encapsulate the results the authors introduce an overall forecast score combining metrics for all parameters, atmospheric levels, and forecast lead times. The first result suggests that removing secondary satellites results in significant degradation of the forecast. This is unexpected since it is generally assumed that secondary sensors contribute to system’s robustness but not necessarily to forecast performance. Second, losing the afternoon orbit on top of losing secondary satellites further degrades forecast performances by a significant margin. Finally, losing extratropical RO observations on top of losing secondary satellites also negatively impacts the forecast performances, but to a lesser degree. These results provide a benchmark that will allow for the assessment of the added value of projects being implemented at NOAA in support of mitigation strategies designed to alleviate the negative impacts associated with these data gaps, and additionally help NOAA to define requirements of the future global observing system architecture.

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B. S. Sandeepan, V. G. Panchang, S. Nayak, K. Krishna Kumar, and J. M. Kaihatu

Abstract

The performance of the Weather Research and Forecasting (WRF) Model is examined for the region around Qatar in the context of surface winds. The wind fields around this peninsula can be complicated owing to its small size, to a complex pattern of land and sea breezes influenced by the prevailing shamal winds, and to its dry and arid nature. Modeled winds are verified with data from 19 land stations and two offshore buoys. A comparison with these data shows that nonlocal planetary boundary layer (PBL) schemes generally perform better than local schemes over land stations during the daytime, when convective conditions prevail; at nighttime, over land and over water, both schemes yield similar results. Among other parameters, modifications to standard USGS land-use descriptors were necessary to reduce model errors. The RMSE values are comparable to those reported elsewhere. Simulated winds, when used with a wave model, result in wave heights comparable to buoy measurements. Furthermore, WRF results, confirmed by data, show that at times sea breezes develop from both coasts, leading to convergence in the middle of the country; at other times, the large-scale wind impedes the formation of sea breezes on one or both coasts. Simulations also indicate greater land/sea-breeze activity in the summer than in the winter. Differences in the diurnal evolution of surface winds over land and water are found to be related to differences in the boundary layer stability. Overall, the results indicate that the WRF Model as configured here yields reliable simulations and can be used for various practical applications.

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Ross N. Hoffman, V. Krishna Kumar, Sid-Ahmed Boukabara, Kayo Ide, Fanglin Yang, and Robert Atlas

Abstract

The summary assessment metric (SAM) method is applied to an array of primary assessment metrics (PAMs) for the deterministic forecasts of three leading numerical weather prediction (NWP) centers for the years 2015–17. The PAMs include anomaly correlation, RMSE, and absolute mean error (i.e., the absolute value of bias) for different forecast times, vertical levels, geographic domains, and variables. SAMs indicate that in terms of forecast skill ECMWF is better than NCEP, which is better than but approximately the same as UKMO. The use of SAMs allows a number of interesting features of the evolution of forecast skill to be observed. All three centers improve over the 3-yr period. NCEP short-term forecast skill substantially increases during the period. Quantitatively, the effect of the 11 May 2016 NCEP upgrade to the four-dimensional ensemble variational data assimilation (4DEnVar) system is a 7.37% increase in the probability of improved skill relative to a randomly chosen forecast metric from 2015 to 2017. This is the largest SAM impact during the study period. However, the observed impacts are within the context of slowly improving forecast skill for operational global NWP as compared to earlier years. Clearly, the systems lagging ECMWF can improve, and there is evidence from SAMs in addition to the 4DEnVar example that improvements in forecast and data assimilation systems are still leading to forecast skill improvements.

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Ross N. Hoffman, Sid-Ahmed Boukabara, V. Krishna Kumar, Kevin Garrett, Sean P. F. Casey, and Robert Atlas

Abstract

The empirical cumulative density function (ECDF) approach can be used to combine multiple, diverse assessment metrics into summary assessment metrics (SAMs) to analyze the results of impact experiments and preoperational implementation testing with numerical weather prediction (NWP) models. The main advantages of the ECDF approach are that it is amenable to statistical significance testing and produces results that are easy to interpret because the SAMs for various subsets tend to vary smoothly and in a consistent manner. In addition, the ECDF approach can be applied in various contexts thanks to the flexibility allowed in the definition of the reference sample.

The interpretations of the examples presented here of the impact of potential future data gaps are consistent with previously reported conclusions. An interesting finding is that the impact of observations decreases with increasing forecast time. This is interpreted as being caused by the masking effect of NWP model errors increasing to become the dominant source of forecast error.

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Shun Liu, Geoff DiMego, Shucai Guan, V. Krishna Kumar, Dennis Keyser, Qin Xu, Kang Nai, Pengfei Zhang, Liping Liu, Jian Zhang, Kenneth Howard, and Jeff Ator

Abstract

Real-time access to level II radar data became available in May 2005 at the National Centers for Environmental Prediction (NCEP) Central Operations (NCO). Using these real-time data in operational data assimilation requires the data be processed reliably and efficiently through rigorous data quality controls. To this end, advanced radar data quality control techniques developed at the National Severe Storms Laboratory (NSSL) are combined into a comprehensive radar data processing system at NCEP. Techniques designed to create a high-resolution reflectivity mosaic developed at the NSSL are also adopted and installed within the NCEP radar data processing system to generate hourly 3D reflectivity mosaics and 2D-derived products. The processed radar radial velocity and 3D reflectivity mosaics are ingested into NCEP’s data assimilation systems to improve operational numerical weather predictions. The 3D reflectivity mosaics and 2D-derived products are also used for verification of high-resolution numerical weather prediction. The NCEP radar data processing system is described.

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Sid-Ahmed Boukabara, Kayo Ide, Yan Zhou, Narges Shahroudi, Ross N. Hoffman, Kevin Garrett, V. Krishna Kumar, Tong Zhu, and Robert Atlas

Abstract

Observing system simulation experiments (OSSEs) are used to simulate and assess the impacts of new observing systems planned for the future or the impacts of adopting new techniques for exploiting data or for forecasting. This study focuses on the impacts of satellite data on global numerical weather prediction (NWP) systems. Since OSSEs are based on simulations of nature and observations, reliable results require that the OSSE system be validated. This validation involves cycles of assessment and calibration of the individual system components, as well as the complete system, with the end goal of reproducing the behavior of real-data observing system experiments (OSEs). This study investigates the accuracy of the calibration of an OSSE system—here, the Community Global OSSE Package (CGOP) system—before any explicit tuning has been performed by performing an intercomparison of the OSSE summary assessment metrics (SAMs) with those obtained from parallel real-data OSEs. The main conclusion reached in this study is that, based on the SAMs, the CGOP is able to reproduce aspects of the analysis and forecast performance of parallel OSEs despite the simplifications employed in the OSSEs. This conclusion holds even when the SAMs are stratified by various subsets (the tropics only, temperature only, etc.).

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Sid-Ahmed Boukabara, Isaac Moradi, Robert Atlas, Sean P. F. Casey, Lidia Cucurull, Ross N. Hoffman, Kayo Ide, V. Krishna Kumar, Ruifang Li, Zhenglong Li, Michiko Masutani, Narges Shahroudi, Jack Woollen, and Yan Zhou

Abstract

A modular extensible framework for conducting observing system simulation experiments (OSSEs) has been developed with the goals of 1) supporting decision-makers with quantitative assessments of proposed observing systems investments, 2) supporting readiness for new sensors, 3) enhancing collaboration across the community by making the most up-to-date OSSE components accessible, and 4) advancing the theory and practical application of OSSEs. This first implementation, the Community Global OSSE Package (CGOP), is for short- to medium-range global numerical weather prediction applications. The CGOP is based on a new mesoscale global nature run produced by NASA using the 7-km cubed sphere version of the Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model and the January 2015 operational version of the NOAA global data assimilation (DA) system. CGOP includes procedures to simulate the full suite of observing systems used operationally in the global DA system, including conventional in situ, satellite-based radiance, and radio occultation observations. The methodology of adding a new proposed observation type is documented and illustrated with examples of current interest. The CGOP is designed to evolve, both to improve its realism and to keep pace with the advance of operational systems.

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Parthasarathi Mukhopadhyay, Peter Bechtold, Yuejian Zhu, R. Phani Murali Krishna, Siddharth Kumar, Malay Ganai, Snehlata Tirkey, Tanmoy Goswami, M. Mahakur, Medha Deshpande, V. S. Prasad, C.J. Johny, Ashim Mitra, Raghavendra Ashrit, Abhijit Sarkar, Sahadat Sarkar, Kumar Roy, Elphin Andrews, Radhika Kanase, Shilpa Malviya, S. Abhilash, Manoj Domkawle, S. D. Pawar, Ashu Mamgain, V. R. Durai, Ravi S. Nanjundiah, Ashish K. Mitra, E. N. Rajagopal, M. Mohapatra, and M. Rajeevan

Abstract

During August 2018 and 2019 the southern state of India, Kerala received unprecedented heavy rainfall which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state of the art global prediction systems, the National Centre for Environmental Prediction (NCEP) based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium Range Weather Forecast (ECMWF) integrated forecast system (IFS) and the Unified Model based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF).

Satellite, radar and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range, sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the ERA5 (ECMWF Reanalyses version 5) reanalyses and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column integrated precipitable water tendency, however is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019.

Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skilful than the deterministic forecasts, as they were able to predict rainfall anomalies (>3 standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

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