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238 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surface heat flux and its association with the MJO in the tropical western Pacific using ARM observations

The Madden-Julian oscillation (MJO) is a major atmospheric phenomenon in the tropics that moves eastward every 20 to 100 days. It brings heavy rain and strong winds, influencing extreme weather events far beyond the tropics – including flooding, hurricanes, tornadoes and heavy snow in the United States. Therefore, an improved understanding of the MJO is critical for enhancing weather forecasts and supporting better decision-making for communities, emergency managers, and the private sector. Past studies, based on short-term observations or long-term model data, have emphasized the dominant role of atmospheric humidity in driving the MJO. Our research, using long-term observations (2000-2014) from three U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) sites in the tropical western Pacific (Manus, Nauru, and Darwin) confirms those past findings. However, we also show that atmospheric temperature, especially in the mid and upper troposphere, also plays a key role in the MJO’s evolution from its quiet (suppressed) to convective (active) phase. These findings provide valuable insights into how the MJO evolves and may help evaluate and improve weather and climate models. Our study also examined how the exchange of heat between the Earth's surface and the atmosphere, called surface heat flux, interacts with the MJO. This flux includes components such as latent heat (related to evaporation), sensible heat (related to temperature difference between surface and atmosphere), and radiation. We found that modulation in MJO convection is well connected to surface heat flux over the tropical western Pacific. One surface heat flux component often overlooked is the sensible heat flux due to precipitation (Q P ). Since falling raindrops are typically cooler than the land or ocean surface, they can cool the surface and affect local weather. To test the impact of Q P on convection during MJO, we incorporated it into a weather model, and ran simulations for two periods: April 2009 (when the MJO was active) and June 2006 (when it was not). Including Q P reduced errors in simulating the daily peak of rainfall—by 83% during the strong MJO and 23% during the inactive phase. It also improved the timing of peak daily rainfall and reduced the overall precipitation error by about 10%. These improvements were especially noticeable during periods of heavy rainfall. Our results suggest that even small heat exchanges from rainfall can play a significant role in shaping local weather. Accounting for these effects, particularly in regions like the tropical islands, can lead to more accurate simulations of the MJO-associated precipitation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

Hou, Arthur Y.↗
NASA NTRS1997

The Climate Signal in Regional Moisture Fluxes: A Comparison of Three Global Data Assimilation Products

This study assesses the quality of estimates of climate variability in moisture flux and convergence from three assimilated data sets: two are reanalysis products generated at the Goddard Data Assimilation Office (DAO) and the National Centers for Environmental Prediction/National Centers for Atmospheric Research (NCEPJNCAR), and the third consists of the operational analyses generated at the European Center for Medium Range Forecasts (ECMWF). The regions under study (the United States Great Plains, the Indian monsoon region, and Argentina east of the Andes) are characterized by frequent low level jets (LLJs) and other interannual low level wind variations tied to the large-scale flow. While the emphasis is on the reanalysis products, the comparison with the operational product is provided to help assess the improvements gained from a fixed analysis system. All three analyses capture the main moisture flux anomalies associated with selected extreme climate (drought and flood) events during the period 1985-93. The correspondence is strongest over the Great Plains and weakest over the Indian monsoon region reflecting differences in the observational coverage. For the reanalysis products, the uncertainties in the lower tropospheric winds is by far the dominant source of the discrepancies in the moisture flux anomalies in the middle latitude regions. Only in the Indian Monsoon region, where interannual variability in the low level winds is comparatively small, does the moisture bias play a substantial role. In contrast, the comparisons with the operational product show differences in moisture which are comparable torhe differences in the wind in all three regions. Compared with the fluxes, the anomalous moisture convergences show substantially larger differences among the three products. The best agreement occurs over the Great Plains region where all three products show vertically-integrated moisture convergence during the floods and divergence during the drought with differences in magnitude of about 25%. The reanalysis products, in particular, show good agreement in depicting the different roles of the mean flow and transients during the flood and drought periods. Differences between the three products in the other two regions exceed 100% reflecting differences in the low level jets and the large scale circulation patterns. The operational product tends to have locally larger amplitude convergence fields which average out in area-mean budgets: this appears to be at least in part due to errors in the surface pressure fields and aliasing from the higher resolution of the original ECMWF fields. On average, the reanalysis products show higher coherence with each other than with the operational product in the estimates of interannual variability. This result is less clear in the Indian monsoon region where differences in the input observations appears to be an important factor. The agreement in the anomalous convergence patterns is, however, still rather poor even over relatively data dense regions such as the United States Great Plains. These differences are attributed to deficiencies in the assimilating GCM's representations of the planetary boundary layer and orography, and a global observing system incapable of resolving the highly confined low level winds associated with the climate anomalies.

Min, Wei↗
NASA NTRS2009

The Transition of High-Resolution NASA MODIS Sea Surface Temperatures into the WRF Environmental Modeling System

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) composite at 2-km resolution that has been implemented in version 3 of the National Weather Service (NWS) Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). The WRF EMS is a complete, full physics numerical weather prediction package that incorporates dynamical cores from both the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). The installation, configuration, and execution of either the ARW or NMM models is greatly simplified by the WRF EMS to encourage its use by NWS Weather Forecast Offices (WFOs) and the university community. The WRF EMS is easy to run on most Linux workstations and clusters without the need for compilers. Version 3 of the WRF EMS contains the most recent public release of the WRF-NMM and ARW modeling system (version 3 of the ARW is described in Skamarock et al. 2008), the WRF Pre-processing System (WPS) utilities, and the WRF Post-Processing program. The system is developed and maintained by the NWS National Science Operations Officer Science and Training Resource Coordinator. To initialize the WRF EMS with high-resolution MODIS SSTs, SPoRT developed the composite product consisting of MODIS SSTs over oceans and large lakes with the NCEP Real-Time Global (RTG) filling data over land points. Filling the land points is required due to minor inconsistencies between the WRF land-sea mask and that used to generate the MODIS SST composites. This methodology ensures a continuous field that adequately initializes all appropriate arrays in WRF. MODIS composites covering the Gulf of Mexico, western Atlantic Ocean and the Caribbean are generated daily at 0400, 0700, 1600, and 1900 UTC corresponding to overpass times of the NASA Aqua and Terra polar orbiting satellites. The MODIS SST product is output in gridded binary-1 (GRIB-1) data format for a seamless incorporation into WRF via the WPS utilities. The full-resolution, 1-km MODIS product is sub-sampled to 2-km grid spacing due to limitations in handling very large dimensions in the GRIB-1 data format. The GRIB-1 files are posted online at ftp://ftp.nsstc.org/sstcomp/WRF/, which is directly accessed by the WRF EMS scripts. The MODIS SST composites are also downloaded to the EMS data server, which is accessible by the WRF EMS users and NWS WFOs. The SPoRT MODIS SST composite provides the model with superior detail of the ocean gradients around Florida and surrounding waters, whereas the operational RTG SST typically depicts a relatively smooth field and is not able to capture sharp horizontal gradients in SST. Differences of 2-3 C are common over small horizontal distances, leading to enhanced SST gradients on either side of the Gulf Stream and along the edges of the cooler shelf waters. These sharper gradients can in turn produce atmospheric responses in simulated temperature and wind fields as depicted in LaCasse et al. Differences in atmospheric verification statistics over a several month study were generally small in the vicinity of south Florida; however, the validation of SSTs at specific buoy locations revealed important improvements in the biases and RMS errors, especially in the vicinity of the cooler shelf waters off the east-central Florida coast. A current weakness in the MODIS SST product is the occurrence of occasional discontinuities caused by high latency in SST coverage due to persistent cloud cover. An enhanced method developed by Jedlovec et al. (2009, GHRSST User Symposium) reduces the occurrence of these problems by adding Advanced Microwave Scanning Radiometer -- EOS (AMSR-E) SST data to the compositing process. Enhanced SST composites are produced over the ocean regions surrounding the Continental U.S. at four times each day corresponding to Terra and Aqua equator crossing times. For a given day and overpass time, both MODInd AMSR-E data from the previous seven days form a collection used in the compositing. At each MODIS pixel, cloud-free SST values from the collection are used to form a weighted average based on their latency (number of days from the current day). In this way, recent SST data are given more weight than older data. One of the primary issues involved in incorporating the AMSR-E microwave data in the composites is the tradeoff between the decreased spatial resolution of the AMSR-E data (25 km) and the increased coverage due to its near all-weather capability. Currently, the AMSR-E is given a weight of 20% compared to MODIS data, thereby preserving the spatial structure observed in the MODIS data. Day-time (night-time) AMSR-E SST data from Aqua are used with both Terra and Aqua MODIS day-time (night-time) SST data sets.

Case, Jonathan L.↗