Search Results

You are looking at 1 - 10 of 13 items for

  • Author or Editor: Lidia Cucurull x
  • Refine by Access: All Content x
Clear All Modify Search
Lidia Cucurull
and
Richard A. Anthes

Abstract

As the U.S. polar-orbiting satellites NOAA-15, -18, and -19 and NASA’s Aqua satellite reach the ends of their lives, there may be a loss in redundancy between their microwave (MW) soundings, and the Advanced Technology Microwave Sounder (ATMS) on the Suomi–National Polar-Orbiting Partnership (NPP) satellite. With the expected delay in the launch of the next generation of U.S. polar-orbiting satellites, there may be a loss in at least some of the U.S. MW data. There may also be a significant decrease in the number of radio occultation (RO) observations. The mainstay of the global RO system, the COSMIC constellation of six satellites is already past the end of its nominal lifetime. A replacement of RO soundings in the tropics is planned with the launch of COSMIC-2 satellites in 2016. However, the polar constellation of COSMIC-2 will not be launched until 2018 or 2019, and complete funding for this constellation is not assured. Using the NCEP operational forecast system, forecasts for March–April 2013 are carried out in which various combinations of the U.S. MW and all RO soundings are removed. The main results are that the forecasts are only slightly degraded in the Northern Hemisphere, even with all of these observations removed. The decrease in accuracy is considerably greater in the Southern Hemisphere, where the greatest forecast degradation occurs when the RO observations are removed. Overall, these results indicate that the possible gap in RO observations is potentially more significant than the possible gap in the U.S. MW data.

Full access
Sean P. F. Casey
and
Lidia Cucurull

Abstract

The impact of low data latency is assessed using observations assimilated into the NCEP Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS). Operationally, a full dataset is used to generate short-term (9-h) forecasts used as the background state for the next cycle, and a limited dataset with fewer observations is used for long-term (16-day) forecasts due to time constraints that exist in an operational setting. In this study, the sensitivity of the global weather forecast skill to the use of the full and limited datasets in both the short- and long-term forecasts (out to 10 days only) is evaluated. The results show that using the full dataset for long-term forecasts yields a slight improvement in forecast skill, while using the limited dataset for short-term forecasts yields a significant degradation. This degradation is primarily attributed to a decrease of in situ observations rather than remotely sensed observations, though no individual observation type captures the amount of degradation noted when all observations are limited. Furthermore, limiting individual types of in situ observations (aircraft, marine, rawinsonde) does not result in the level of degradation noted when limiting all in situ observations, demonstrating the importance of data redundancy in an operational observational system.

Significance Statement

Millions of observations are used in global models every day to understand the state of the atmosphere. These observations rely on quick transmission from observation source to weather centers for inclusion in operational models. For this study, we test how different groups of observations, which arrive at the model center at different times, impact the model forecast. We find that by not using the observations that take longer to arrive at the weather centers, the forecast is much worse, showing the importance of quick transmission of observations. Direct observations (those measured within the atmosphere) have a greater impact than remote observations (those viewed from afar, such as by satellites). However, no single observation type by itself causes a poor forecast by being limited, showing the importance of using different types of observations to capture the state of the atmosphere.

Restricted access
Michael J. Mueller
,
Bachir Annane
,
S. Mark Leidner
, and
Lidia Cucurull

Abstract

An observing system experiment was conducted to assess the impact of wind products derived from the Cyclone Global Navigation Satellite System (CYGNSS) on tropical cyclone track, maximum 10-m wind speed V max, and minimum sea level pressure forecasts. The experiment used a global data assimilation and forecast system, and the impact of both CYGNSS-derived scalar and vector wind retrievals was investigated. The CYGNSS-derived vector wind products were generated by optimally combining the scalar winds and a gridded a priori vector field. Additional tests investigated the impact of CYGNSS data on a regional model through the impact of lateral boundary and initial conditions from the global model during the developmental phase of Hurricane Michael (2018). In the global model, statistically significant track forecast improvements of 20–40 km were found in the first 60 h. The V max forecasts showed some significant degradations of ~2 kt at a few lead times, especially in the first 24 h. At most lead times, impacts were not statistically significant. Degradations in V max for Hurricane Michael in the global model were largely attributable to a failure of the CYGNSS-derived scalar wind test to produce rapid intensification in the forecast initialized at 0000 UTC 7 October. The storm in this test was notably less organized and symmetrical than in the control and CYGNSS-derived vector wind test. The regional model used initial and lateral boundary conditions from the global control and CYGNSS scalar wind tests. The regional forecasts showed large improvements in track, V max, and minimum sea level pressure.

Full access
Tanya R. Peevey
,
Jason M. English
,
Lidia Cucurull
,
Hongli Wang
, and
Andrew C. Kren

Abstract

Severe weather events can have a significant impact on local communities because of the loss of life and property. Forecast busts associated with high-impact weather events have been attributed to initial condition errors over data-sparse regions, such as the Pacific Ocean. Numerous flight campaigns have found that targeted observations over these areas can improve forecasts. To better understand the impacts of measurement type and sampling domains on forecast performance, observing system simulation experiments are performed using the National Centers for Environmental Prediction Global Forecast System (GFS) with hybrid 3DEnVar data assimilation and the ECMWF T511 nature run. First, three types of simulated perfect dropsonde observations (temperature, specific humidity, and wind) are assimilated into the GFS over a large idealized sampling domain covering the Pacific Ocean. For the three winter storms studied, forecast error was found to be significantly reduced with all three types of measurements providing the most benefit ( %–15% reduction in error). Instances when forecasts are not improved are investigated and concluded to be due to challenging meteorological structures, such as cutoff lows and interactions with atmospheric structures outside the sampling domain. Second, simulated dropsondes are assimilated over sensitive areas and flight tracks established using the ensemble transform sensitivity (ETS) technique. For all three winter storms, forecast error is reduced up to 5%, which is less than that found using an idealized domain. These results suggest that targeted observations over the Pacific Ocean may provide a small improvement to winter storm forecasts over the United States.

Full access
Michael J. Mueller
,
Andrew C. Kren
,
Lidia Cucurull
,
Sean P. F. Casey
,
Ross N. Hoffman
,
Robert Atlas
, and
Tanya R. Peevey

Abstract

A global observing system simulation experiment (OSSE) was used to assess the potential impact of a proposed Global Navigation Satellite System (GNSS) radio occultation (RO) constellation on tropical cyclone (TC) track, maximum 10-m wind speed (V max), and integrated kinetic energy (IKE) forecasts. The OSSE system was based on the 7-km NASA nature run and simulated RO refractivity determined by the spatial distribution of observations from the original planned (i.e., including both equatorial and polar orbits) Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2). Data were assimilated using the NOAA operational weather analysis and forecasting system. Three experiments generated global TC track, V max, and IKE forecasts over 6 weeks of the North Atlantic hurricane season in the North Atlantic, east Pacific, and west Pacific basins. Confidence in our results was bolstered because track forecast errors were similar to those of official National Hurricane Center forecasts, and V max errors and IKE errors showed similar results. GNSS-RO assimilation did not significantly impact global track forecasts, but did slightly degrade V max and IKE forecasts in the first 30–60 h of lead time. Global forecast error statistics show adding or excluding explicit random errors to RO profiles made little difference to forecasts. There was large forecast-to-forecast variability in RO impact. For two cases studied in depth, track and V max improvements and degradations were traced backward through the previous 24 h of assimilation cycles. The largest V max degradation was traced to particularly good control analyses rather than poor analyses caused by GNSS-RO.

Free access
Sid-Ahmed Boukabara
,
Kayo Ide
,
Narges Shahroudi
,
Yan Zhou
,
Tong Zhu
,
Ruifang Li
,
Lidia Cucurull
,
Robert Atlas
,
Sean P. F. Casey
, and
Ross N. Hoffman

Abstract

The simulation of observations—a critical Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) component—is validated first by a comparison of error-free simulated observations for the first 24 h at the start of the nature run (NR) to the real observations for those sensors that operated during that period. Sample results of this validation are presented here for existing low-Earth-orbiting (LEO) infrared (IR) and microwave (MW) brightness temperature (BT) observations, for radio occultation (RO) bending angle observations, and for various types of conventional observations. For sensors not operating at the start of the NR, a qualitative validation is obtained by comparing geographic and statistical characteristics of observations over the initial day for such a sensor and an existing similar sensor. The comparisons agree, with no significant unexplained bias, and to within the uncertainties caused by real observation errors, time and space collocation differences, radiative transfer uncertainties, and differences between the NR and reality. To validate channels of a proposed future MW sensor with no equivalent existing spaceborne sensor channel, multiple linear regression is used to relate these channels to existing similar channels. The validation then compares observations simulated from the NR to observations predicted by the regression relationship applied to actual real observations of the existing channels. Overall, the CGOP simulations of error-free observations from conventional and satellite platforms that make up the global observing system are found to be reasonably accurate and suitable as a starting point for creating realistic simulated observations for OSSEs. These findings complete a critical step in the CGOP validation, thereby reducing the caveats required when interpreting the OSSE results.

Full access
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.

Full access
Xubin Zeng
,
Robert Atlas
,
Ronald J. Birk
,
Frederick H. Carr
,
Matthew J. Carrier
,
Lidia Cucurull
,
William H. Hooke
,
Eugenia Kalnay
,
Raghu Murtugudde
,
Derek J. Posselt
,
Joellen L. Russell
,
Daniel P. Tyndall
,
Robert A. Weller
, and
Fuqing Zhang

Abstract

The NOAA Science Advisory Board appointed a task force to prepare a white paper on the use of observing system simulation experiments (OSSEs). Considering the importance and timeliness of this topic and based on this white paper, here we briefly review the use of OSSEs in the United States, discuss their values and limitations, and develop five recommendations for moving forward: national coordination of relevant research efforts, acceleration of OSSE development for Earth system models, consideration of the potential impact on OSSEs of deficiencies in the current data assimilation and prediction system, innovative and new applications of OSSEs, and extension of OSSEs to societal impacts. OSSEs can be complemented by calculations of forecast sensitivity to observations, which simultaneously evaluate the impact of different observation types in a forecast model system.

Free access
Xubin Zeng
,
Robert Atlas
,
Ronald J. Birk
,
Frederick H. Carr
,
Matthew J. Carrier
,
Lidia Cucurull
,
William H. Hooke
,
Eugenia Kalnay
,
Raghu Murtugudde
,
Derek J. Posselt
,
Joellen L. Russell
,
Daniel P. Tyndall
,
Robert A. Weller
, and
Fuqing Zhang
Full access
Gary A. Wick
,
Jason P. Dunion
,
Peter G. Black
,
John R. Walker
,
Ryan D. Torn
,
Andrew C. Kren
,
Altug Aksoy
,
Hui Christophersen
,
Lidia Cucurull
,
Brittany Dahl
,
Jason M. English
,
Kate Friedman
,
Tanya R. Peevey
,
Kathryn Sellwood
,
Jason A. Sippel
,
Vijay Tallapragada
,
James Taylor
,
Hongli Wang
,
Robbie E. Hood
, and
Philip Hall
Full access