5830 University Research Court
5830 University Research Court
L'Heureux M.L.,5830 University Research Court |
Lee S.,Pennsylvania State University |
Lyon B.,International Research Institute for Climate and Society
Nature Climate Change | Year: 2013
The Pacific Walker circulation is a large overturning cell that spans the tropical Pacific Ocean, characterized by rising motion (lower sea-level pressure) over Indonesia and sinking motion (higher sea level-pressure) over the eastern Pacific. Fluctuations in the Walker circulation reflect changes in the location and strength of tropical heating, so related circulation anomalies have global impacts. On interannual timescales, the El Niño/Southern Oscillation accounts for much of the variability in the Walker circulation, but there is considerable interest in longer-term trends and their drivers, including anthropogenic climate change. Here, we examine sea-level pressure trends in ten different data sets drawn from reanalysis, reconstructions and in situ measurements for 1900-2011. We show that periods with fewer in situ measurements result in lower signal-to-noise ratios, making assessments of sea-level pressure trends largely unsuitable before about the 1950s. Multidecadal trends evaluated since 1950 reveal statistically significant, negative values over the Indonesian region, with weaker, positive trends over the eastern Pacific. The overall trend towards a stronger, La Niña-like Walker circulation is nearly concurrent with the observed increase in global average temperatures, thereby justifying closer scrutiny of how the Pacific climate system has changed in the historical record. © 2013 Macmillan Publishers Limited. All rights reserved.
Padula F.,GeoThinkTank LLC |
Cao C.,5830 University Research Court
Applied Optics | Year: 2015
The Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data record (SDR) product achieved validated maturity status in March 2014 after roughly two years of on-orbit characterization (S-NPP spacecraft launched on 28 October 2011). During post-launch analysis the VIIRS Sea Surface Temperature (SST) Environmental Data Record (EDR) team observed an anomalous striping pattern in the daytime SST data. Daytime SST retrievals use the two VIIRS long-wave infrared bands: M15 (10.7 μm) and M16 (11.8 μm). To assess possible root causes due to detector-level spectral response function (SRF) effects, a study was conducted to compare the radiometric response of the detector-level and operationalband averaged SRFs of VIIRS bands M15 and M16. The study used simulated hyperspectral blackbody radiance data and clear-sky ocean hyperspectral radiances under different atmospheric conditions. It was concluded that the SST product is likely impacted by small differences in detector-level SRFs and that if users require optimal radiometric performance, detector-level processing is recommended for both SDR and EDR products. Future work should investigate potential SDR product improvements through detector-level processing in support of the generation of Suomi NPP VIIRS climate quality SDRs. © 2015 Optical Society of America.
Liu R.-F.,Central Weather Bureau CWB |
Wang W.,5830 University Research Court
Climate Dynamics | Year: 2015
In this paper we analyze the multi-week prediction bias and skill from the National Centers for Environment Prediction (NCEP) Climate Forecast System version 2 (CFSv2) based on its hindcasts for 1999–2012. The analyses focus on the prediction of the rainfall variability over South-East Asia during boreal warm seasons and the dependence of the prediction on the activity of intrasesaonal leading modes. It is shown that the prediction skill measured by anomaly correlation is comparable between the total anomalies and intraseasonal anomalies during the first 2 weeks. After week 2, the prediction skill drops substantially and the skill for total anomalies is largely from the prediction for the interannual variability. Moreover, the forecast skill tends to be higher when the amplitude of the Madden–Julian Oscillation and the Boreal Summer Intraseasonal Oscillation (BSISO) is larger, especially for the BSISO. It is noted that the prediction skill over South-East Asia depends on the phase of the BSISO. One deficiency in the CFSv2 is that the northward propagation of the forecast BSISO is generally slower than the observed. © 2014, Springer-Verlag (outside the USA).
Kumar A.,5830 University Research Court |
Murtugudde R.,University of Maryland University College
Current Opinion in Environmental Sustainability | Year: 2013
In this essay, the common thread of limits of predictability and uncertainty that permeate across weather and climate prediction and projections is discussed in the context of developing a strategy for 'seamless' communication and utilization of uncertain information in decision making. In understanding why uncertainty is an unavoidable trait of predictions in the first place, a useful concept is the separation of the Earth System (ES) into internal and external components. This separation allows one to first, recognize that for prediction at all time-scales, the inherent source for limits on predictability is due to the divergence of forecasts from a cloud of initial conditions, and second, thereby recognize that the fundamental source of uncertainty (or unpredictability) is limited by our ability to specify initial conditions for the internal component with perfect accuracy.The unavoidability of uncertainty in predictions, and accepting this fact could be advantageous in the ongoing discussions on how to communicate climate projections and the associated uncertainties by learning from the knowledge base that exists for communicating similar information on weather and seasonal predictions that are generated on a much more frequent basis. Similarly, decision-support systems for developing adaptation and mitigation strategies can use predictions on shorter range as a test-bed to hone their strategies to incorporate predictive uncertainty when dealing with longer-range projections. By practicing the use of decision making tools and the incorporation of uncertain predictions on weather and seasonal time scale, decision makers can improve their level of comfort in accepting uncertainty inherent in longer range predictions and projections on a much less infrequent basis. In this paradigm, evolving strategy for seamless predictions can be blended with a strategy for seamless communication of uncertain information and also with seamless application of decision support systems. © 2013.
Chai T.,5830 University Research Court |
Chai T.,University of Maryland University College |
Draxler R.R.,5830 University Research Court
Geoscientific Model Development | Year: 2014
Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance. © Author(s) 2014. CC Attribution 3.0 License.
Liang X.,5830 University Research Court |
Liang X.,Cooperative Institute for Research in the Atmospheres CIRA |
Ignatov A.,5830 University Research Court
Journal of Geophysical Research: Oceans | Year: 2013
Monitoring of IR Clear-Sky Radiances over Oceans for SST (MICROS; www.star.nesdis.noaa.gov/sod/sst/micros) is NESDIS near-real time web-based radiance monitoring system. It analyzes Model (Community Radiative Transfer Model, CRTM) minus Observation (M-O) biases in brightness temperatures (BT) in three bands centered at 3.7 (IR37), 11 (IR11), and 12 μm (IR12), for several AVHRR (NOAA-16, -17, -18, -19, Metop-A, -B), VIIRS (Suomi National Polar Partnership, S-NPP), and MODIS (Terra, Aqua) sensors. Double-differences (DD) are employed to check BTs for radiometric stability and consistency. All sensors are stable, with the exception of two AVHRRs, onboard NOAA-16 and to a lesser extent NOAA-18, and generally consistent. VIIRS onboard S-NPP, launched in October 2011, is well in-family, especially after its calibration was fine-tuned on 7 March 2012. MODIS M-O biases were initially out-of-family by up to -0.6 K, due to incorrect CRTM transmittance coefficients. Following MICROS feedback, CRTM Team updated coefficients and brought MODIS back in-family. Terra and Aqua BTs are very consistent in IR11 and IR12 but show cross-platform bias of 0.3 K in IR37, likely attributed to MODIS characterization. Work with MODIS Characterization Support Team is underway to resolve this. Initial analyses of AVHRR onboard Metop-B launched in September 2012 suggest that its BTs are offset from Metop-A by up to ∼0.3 K. Overall, MICROS DDs are well suited to evaluate the sensors stability, but dedicated effort is needed to ensure consistent radiative transfer modeling (RTM) calculations for various sensors before DDs can be used in Global Space-based Inter-Calibration System (GSICS) quantitative applications. Key Points The MICROS DD has an excellent potential to monitor BTs for cross-consistency. AVHRR, MODIS, and VIIRS are generally stable and consistent. Consistent RTM calculations are important for quantitative DD analyses ©2013. American Geophysical Union. All Rights Reserved.
Jha B.,5830 University Research Court |
Jha B.,Wyle |
Hu Z.-Z.,5830 University Research Court |
Kumar A.,5830 University Research Court
Climate Dynamics | Year: 2014
This work documents the diversity in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models in simulating different aspects of sea surface temperature (SST) variability, particularly those associated with the El Niño-Southern Oscillation (ENSO), as well as the impact of low-frequency variations on the ENSO variability and its global teleconnection. The historical simulations (1870-2005) include 10 models with ensemble member ranging from 3 to 10 that are forced with observed atmospheric composition changes reflecting both natural and anthropogenic forcings. It is shown that the majority of the CMIP5 models capture the relative large SST anomaly variance in the tropical central and eastern Pacific, as well as in North Pacific and North Atlantic. The frequency of ENSO is not well captured by almost all models, particularly for the period of 5-6 years. The low-frequency variations in SST caused by external forcings affect the SST variability and also modify the global teleconnection of ENSO. The models reproduce the global averaged SST low-frequency variations, particularly since 1970s. However, majority of the models are unable to correctly simulate the spatial pattern of the observed SST trends. These results suggest that it is still a challenge to reproduce the features of global historical SST variations with the state-of-the-art coupled general circulation model. © 2013 Springer-Verlag Berlin Heidelberg.
Kumar A.,5830 University Research Court |
Wang H.,5830 University Research Court
Climate Dynamics | Year: 2015
Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean–atmosphere variability, for example, Pacific Decadal Oscillation (PDO) or Atlantic Multi-decadal Oscillation (AMO). So far, assessments of the skill of atmospheric and terrestrial variability in decadal predictions, however, have not been encouraging. Similarly, in the context of seasonal climate variability, teleconnections between SSTs associated with PDO and AMO and terrestrial climate have also been noted, but the same SST information used in predictive mode has failed to demonstrate convincing gains in skill. Are these results an artifact of model biases, or more a consequence of some fundamental property of coupled evolution of ocean–atmosphere system in extratropical latitudes, and the manner in which extratropical SST anomalies modulate (or constrain) atmospheric variability? Based on revisiting an analysis of a simple model that replicates the essential characteristics of coupled ocean–atmosphere interaction in extratropical latitudes, it is demonstrated that lack of additional skill in predicting atmospheric and terrestrial variability is more a consequence of fundamental characteristics of coupled evolution of ocean–atmosphere system. The results based on simple models are also substantiated following an analysis of a set of seasonal hindcasts with a fully coupled model. © 2014, Springer-Verlag (outside the USA).
Wang W.,5830 University Research Court |
Hung M.-P.,5830 University Research Court |
Weaver S.J.,5830 University Research Court |
Kumar A.,5830 University Research Court |
Fu X.,University of Hawaii at Manoa
Climate Dynamics | Year: 2014
The Madden-Julian Oscillation (MJO) is the primary mode of tropical intraseasonal climate variability and has significant modulation of global climate variations and attendant societal impacts. Advancing prediction of the MJO using state of the art observational data and modeling systems is thus a necessary goal for improving global intraseasonal climate prediction. MJO prediction is assessed in the NOAA Climate Forecast System version 2 (CFSv2) based on its hindcasts initialized daily for 1999-2010. The analysis focuses on MJO indices taken as the principal components of the two leading EOFs of combined 15°S-15°N average of 200-hPa zonal wind, 850-hPa zonal wind and outgoing longwave radiation at the top of the atmosphere. The CFSv2 has useful MJO prediction skill out to 20 days at which the bivariate anomaly correlation coefficient (ACC) drops to 0.5 and root-mean-square error (RMSE) increases to the level of the prediction with climatology. The prediction skill also shows a seasonal variation with the lowest ACC during the boreal summer and highest ACC during boreal winter. The prediction skills are evaluated according to the target as well as initial phases. Within the lead time of 10 days the ACC is generally greater than 0.8 and RMSE is less than 1 for all initial and target phases. At longer lead time, the model shows lower skills for predicting enhanced convection over the Maritime Continent and from the eastern Pacific to western Indian Ocean. The prediction skills are relatively higher for target phases when enhanced convection is in the central Indian Ocean and the central Pacific. While the MJO prediction skills are improved in CFSv2 compared to its previous version, systematic errors still exist in the CFSv2 in the maintenance and propagation of the MJO including (1) the MJO amplitude in the CFSv2 drops dramatically at the beginning of the prediction and remains weaker than the observed during the target period and (2) the propagation in the CFSv2 is too slow. Reducing these errors will be necessary for further improvement of the MJO prediction. © 2013 Springer-Verlag (outside the USA).
Zhu T.,Colorado State University |
Zhu T.,5830 University Research Court |
Weng F.,College Park
Geophysical Research Letters | Year: 2013
The warm-core structures of Hurricane Sandy and other nine tropical cyclones (TCs) are studied using the temperatures retrieved from Advanced Technology Microwave Sounder (ATMS). A new algorithm is developed for the retrieval of atmospheric temperature profiles from the ATMS radiances. Since ATMS observation has a higher spatial resolution and better coverage than its predecessor, Advanced Microwave Sounding Unit-A, the retrieved temperature field explicitly resolves TC warm core throughout troposphere and depicts the cold temperature anomalies in the eyewall and spiral rainbands. Unlike a typical TC, the height of maximum warm core of Hurricane Sandy is very low, but the storm size is quite large. Based on the analysis of 10 TCs in 2012, close correlations are found between ATMS-derived warm core and the TC maximum sustained wind (MSW) or minimum sea level pressure (MSLP). The estimation errors of MSW and MSLP from ATMS-retrieved warm core are 13.5 mph and 13.1 hPa, respectively. Key Points A new algorithm was developed to retrieve atmospheric temperatures from ATMS We studied Hurricane Sandy warm cores, including that of Hurricane Sandy Tropical cyclone intensity was estimated using retrieved temperature anomaly ©2013. American Geophysical Union. All Rights Reserved.