V. Krishna Kumar

Systematic Differences in Aircraft and Radiosonde Temperatures

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 er...

WSR-88D Radar Data Processing at NCEP

AbstractReal-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 numeri...

S4: An O2R/R2O Infrastructure for Optimizing Satellite Data Utilization in NOAA Numerical Modeling Systems: A Step Toward Bridging the Gap between Research and Operations

AbstractIn 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies ...

Potential Gaps in the Satellite Observing System Coverage: Assessment of Impact on NOAA’s Numerical Weather Prediction Overall Skills

AbstractThe 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 remo...

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Description and Usage

AbstractA 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, inclu...

Community Global Observing System Simulation Experiment (OSSE) Package (CGOP): Assessment and Validation of the OSSE System using an OSSE/OSE Intercomparison of Summary Assessment Metrics

AbstractObserving 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 exp...

An Empirical Cumulative Density Function Approach to Defining Summary NWP Forecast Assessment Metrics

AbstractThe 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 bec...