TRMM Latent Heating Retrieval: Applications and Comparisons with Field Campaigns and Large-Scale Analyses

2016 ◽  
Vol 56 ◽  
pp. 2.1-2.34 ◽  
Author(s):  
W.-K. Tao ◽  
Y. N. Takayabu ◽  
S. Lang ◽  
S. Shige ◽  
W. Olson ◽  
...  
Keyword(s):  
Life Cycle ◽  
Large Scale ◽  
Climate Models ◽  
Meteorological Data ◽  
Tropical Rainfall Measuring Mission ◽  
Theoretical Background ◽  
Latent Heating ◽  
Weather And Climate ◽  
Field Campaigns ◽  
The Look

Abstract Yanai and coauthors utilized the meteorological data collected from a sounding network to present a pioneering work in 1973 on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2). Latent heating (LH) is one of the most dominant terms in Q1. Yanai’s paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q1 data have been used to generate the look-up tables in Tropical Rainfall Measuring Mission (TRMM) LH algorithms. A set of algorithms developed for retrieving LH profiles from TRMM-based rainfall profiles is described and evaluated, including details concerning their intrinsic space–time resolutions. Included in the paper are results from a variety of validation analyses that define the uncertainty of the LH profile estimates. Also, examples of how TRMM-retrieved LH profiles have been used to understand the life cycle of the MJO and improve the predictions of global weather and climate models as well as comparisons with large-scale analyses are provided. Areas for further improvement of the TRMM products are discussed.

Retrieval of Latent Heating from TRMM Measurements

2006 ◽  
Vol 87 (11) ◽  
pp. 1555-1572 ◽  
Author(s):  
W.-K. Tao ◽  
E. A. Smith ◽  
R. F. Adler ◽  
Z. S. Haddad ◽  
A. Y. Hou ◽  
...  
Keyword(s):  
Latent Heat ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Tropical Rainfall Measuring Mission ◽  
Phase Changes ◽  
Latent Heating ◽  
Middle Latitudes ◽  
Rate Profile ◽  
The Tropics

Rainfall is a fundamental process within the Earth's hydrological cycle because it represents a principal forcing term in surface water budgets, while its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating well into the middle latitudes. Latent heat production itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics, as well as modify the energetic efficiencies of midlatitude weather systems. This paper highlights the retrieval of latent heating from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American–Japanese space endeavor. Since then, TRMM measurements have been providing credible four-dimensional accounts of rainfall over the global Tropics and subtropics, information that can be used to estimate the space–time structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies for estimating latent heating based on precipitation-rate profile retrievals obtained from TRMM measurements has been under continuous development since the advent of the mission s research program. These algorithms are briefly described, followed by a discussion of the latent heating products that they generate. The paper then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

scholarly journals Effects of climate model radiation, humidity and wind estimates on hydrological simulations

2012 ◽  
Vol 16 (2) ◽  
pp. 305-318 ◽  
Author(s):  
I. Haddeland ◽  
J. Heinke ◽  
F. Voß ◽  
S. Eisner ◽  
C. Chen ◽  
...  
Keyword(s):  
Bias Correction ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Meteorological Data ◽  
Correction Method ◽  
Hydrological Models ◽  
Temperature And Precipitation ◽  
Effects Of Radiation ◽  
Precipitation And Temperature

Abstract. Due to biases in the output of climate models, a bias correction is often needed to make the output suitable for use in hydrological simulations. In most cases only the temperature and precipitation values are bias corrected. However, often there are also biases in other variables such as radiation, humidity and wind speed. In this study we tested to what extent it is also needed to bias correct these variables. Responses to radiation, humidity and wind estimates from two climate models for four large-scale hydrological models are analysed. For the period 1971–2000 these hydrological simulations are compared to simulations using meteorological data based on observations and reanalysis; i.e. the baseline simulation. In both forcing datasets originating from climate models precipitation and temperature are bias corrected to the baseline forcing dataset. Hence, it is only effects of radiation, humidity and wind estimates that are tested here. The direct use of climate model outputs result in substantial different evapotranspiration and runoff estimates, when compared to the baseline simulations. A simple bias correction method is implemented and tested by rerunning the hydrological models using bias corrected radiation, humidity and wind values. The results indicate that bias correction can successfully be used to match the baseline simulations. Finally, historical (1971–2000) and future (2071–2100) model simulations resulting from using bias corrected forcings are compared to the results using non-bias corrected forcings. The relative changes in simulated evapotranspiration and runoff are relatively similar for the bias corrected and non bias corrected hydrological projections, although the absolute evapotranspiration and runoff numbers are often very different. The simulated relative and absolute differences when using bias corrected and non bias corrected climate model radiation, humidity and wind values are, however, smaller than literature reported differences resulting from using bias corrected and non bias corrected climate model precipitation and temperature values.

Relationship between meningitis occurrence and atmospheric conditions over the African meningitis belt

2020 ◽  
Author(s):  
Cheikh Dione ◽  
Mame Diarra Diouf ◽  
Bob Alex Ogwang ◽  
Elijah Adesanya Adefisan ◽  
Steve Woolnough ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Lassa Fever ◽  
Atmospheric Conditions ◽  
Numerical Weather ◽  
Endemic Diseases ◽  
Meningitis Belt ◽  
Weather And Climate

<p> The alternation of seasons over tropical northern Africa is associated with the occurrence of devastating diseases such as meningitis, Lassa fever and malaria. These tropical diseases are associated with specific atmospheric conditions. Thus, meningitis is one of the most endemic diseases observed over this region with a prevalence period up to 7 months (December-June). Previous studies based on the link between atmospheric conditions and the occurrence of meningitis outbreaks have shown that this disease develops under dry and dusty atmospheric conditions which are difficult to represent in numerical weather and climate models. However, the onset, breakup, and sub-seasonal variability of meningitis outbreaks are not well documented. The objective of this study is to identify the local and synoptic drivers favoring the large occurrence of this disease over the meningitis belt in order to improve its predictability by numerical weather and climate models on intra-seasonal and seasonal timescales. This study focuses on two cases studies of meningitis epidemics over Niger in 2009 and 2015. The case study of 2009 started early with a duration of more than eight weeks. The second case study was shorter than the first one. It took three weeks and was observed at the end of the dry season. Based on ERA5 data, surface dust concentration observations and satellite data, a further analysis of the role of climate metrics on the triggering of meningitis epidemics on intra-seasonal timescales at local and large scale atmospheric conditions will be presented.</p>

scholarly journals Using cloud ice flux to parametrise large-scale lightning

2014 ◽  
Vol 14 (12) ◽  
pp. 17817-17856 ◽  
Author(s):  
D. L. Finney ◽  
R. M. Doherty ◽  
O. Wild ◽  
H. Huntrieser ◽  
H. C. Pumphrey ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Tropical Rainfall Measuring Mission ◽  
Reanalysis Data ◽  
Lightning Flash ◽  
Spatial Correlations ◽  
Zonal Distribution ◽  
Annual Total ◽  
Cloud Ice ◽  
Mean Square Errors

Abstract. Lightning is an important natural source of nitrogen oxide especially in the middle and upper troposphere. Hence, it is essential to represent lightning in chemistry transport and coupled chemistry-climate models. Using ERA-Interim meteorological reanalysis data we compare the lightning flash density distributions produced using several existing lightning parametrisations, as well as a new parametrisation developed on the basis of upward cloud ice flux at 440 hPa. The use of ice flux forms a link to the non-inductive charging mechanism of thunderstorms. Spatial and temporal distributions of lightning flash density are compared to tropical and subtropical observations for 2007–2011 from the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission satellite. The well-used lightning flash parametrisation based on cloud-top height has large biases but the derived annual total flash density has a better spatial correlation with the LIS observations than other existing parametrisations. A comparison of flash density simulated by the different schemes shows that the cloud-top height parametrisation has many more instances of moderate flash densities and fewer low and high extremes compared to the other parametrisations. Other studies in the literature have shown that this feature of the cloud-top height parametrisation is in contrast to lightning observations over certain regions. Our new ice flux parametrisation shows a clear improvement over all the existing parametrisations with lower root mean square errors and better spatial correlations with the observations for distributions of annual total, and seasonal and interannual variations. The greatest improvement with the new parametrisation is a more realistic representation of the zonal distribution with a better balance between tropical and subtropical lightning flash estimates. The new parametrisation is appropriate for testing in chemistry transport and chemistry-climate models that use a lightning parametrisation.

scholarly journals Measuring changes in snowpack SWE continuously on a landscape scale using lake water pressure 

2021 ◽  
Author(s):  
Hamish Pritchard ◽  
Daniel Farinotti ◽  
Steven Colwell
Keyword(s):  
Lake Water ◽  
Large Scale ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Water Pressure ◽  
High Mountain ◽  
Alpine Valley ◽  
Weather And Climate ◽  
Forecast Models ◽  
Snow Water

<p>The seasonal snowpack is a globally important water resource that is notoriously difficult to measure. Existing instruments make measurements of falling or accumulating snow water equivalent (SWE) that are susceptible to bias, and most can represent only a point in the landscape. Furthermore the global array of SWE sensors is too sparse and too poorly distributed to be an adequate constraint on snow in weather and climate models. We present a new approach to monitoring snowpack SWE from time series of lake water pressure. We tested our method in the lowland Finnish Arctic and in an alpine valley and high-mountain cirque in Switzerland, and found that we could measure changes in SWE and their uncertainty through snowfalls with little bias and with an uncertainty comparable to or better than that achievable by other instruments. More importantly, our method inherently senses change over the whole lake surface which can be several square kilometres, or hundreds of million of times larger than the aperture of a pluviometer. This large scale makes our measurements directly comparable to the grid cells of weather and climate models. We find, for example, snowfall biases of up to 100% in operational forecast models AROME-Arctic and COSMO-1. Seasonally-frozen lakes are widely distributed at high latitudes and are particularly common in mountain ranges, hence our new method is particularly well suited to the widespread, autonomous monitoring of snow-water resources in remote areas that are largely unmonitored today. This is potentially transformative in reducing uncertainty in regional precipitation and runoff in seasonally-cold climates.</p>

scholarly journals Using cloud ice flux to parametrise large-scale lightning

2014 ◽  
Vol 14 (23) ◽  
pp. 12665-12682 ◽  
Author(s):  
D. L. Finney ◽  
R. M. Doherty ◽  
O. Wild ◽  
H. Huntrieser ◽  
H. C. Pumphrey ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Tropical Rainfall Measuring Mission ◽  
Reanalysis Data ◽  
Lightning Flash ◽  
Spatial Correlations ◽  
Zonal Distribution ◽  
Annual Total ◽  
Cloud Ice ◽  
Mean Square Errors

Abstract. Lightning is an important natural source of nitrogen oxide especially in the middle and upper troposphere. Hence, it is essential to represent lightning in chemistry transport and coupled chemistry–climate models. Using ERA-Interim meteorological reanalysis data we compare the lightning flash density distributions produced using several existing lightning parametrisations, as well as a new parametrisation developed on the basis of upward cloud ice flux at 440 hPa. The use of ice flux forms a link to the non-inductive charging mechanism of thunderstorms. Spatial and temporal distributions of lightning flash density are compared to tropical and subtropical observations for 2007–2011 from the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite. The well-used lightning flash parametrisation based on cloud-top height has large biases but the derived annual total flash density has a better spatial correlation with the LIS observations than other existing parametrisations. A comparison of flash density simulated by the different schemes shows that the cloud-top height parametrisation has many more instances of moderate flash densities and fewer low and high extremes compared to the other parametrisations. Other studies in the literature have shown that this feature of the cloud-top height parametrisation is in contrast to lightning observations over certain regions. Our new ice flux parametrisation shows a clear improvement over all the existing parametrisations with lower root mean square errors (RMSEs) and better spatial correlations with the observations for distributions of annual total, and seasonal and interannual variations. The greatest improvement with the new parametrisation is a more realistic representation of the zonal distribution with a better balance between tropical and subtropical lightning flash estimates. The new parametrisation is appropriate for testing in chemistry transport and chemistry–climate models that use a lightning parametrisation.

Estimates of Tropical Diabatic Heating Profiles: Commonalities and Uncertainties

2010 ◽  
Vol 23 (3) ◽  
pp. 542-558 ◽  
Author(s):  
Samson Hagos ◽  
Chidong Zhang ◽  
Wei-Kuo Tao ◽  
Steve Lang ◽  
Yukari N. Takayabu ◽  
...  
Keyword(s):  
Large Scale ◽  
Diabatic Heating ◽  
Tropical Rainfall Measuring Mission ◽  
Upper Troposphere ◽  
Tropical Oceans ◽  
Field Campaigns ◽  
Heating Profiles ◽  
The Tropics ◽  
Rain Rates

Abstract This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates.

scholarly journals Shallow and Deep Latent Heating Modes over Tropical Oceans Observed with TRMM PR Spectral Latent Heating Data

2010 ◽  
Vol 23 (8) ◽  
pp. 2030-2046 ◽  
Author(s):  
Yukari N. Takayabu ◽  
Shoichi Shige ◽  
Wei-Kuo Tao ◽  
Nagio Hirota
Keyword(s):  
Large Scale ◽  
Deep Convection ◽  
Three Dimensional ◽  
Tropical Rainfall Measuring Mission ◽  
Radiative Heating ◽  
Latent Heating ◽  
Tropical Oceans ◽  
Trmm Precipitation ◽  
Subtropical Oceans ◽  
Cumulus Congestus

Abstract Three-dimensional distributions of the apparent heat source (Q1) − radiative heating (QR) estimated from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) utilizing the spectral latent heating (SLH) algorithm are analyzed. Mass-weighted and vertically integrated Q1 − QR averaged over the tropical oceans is estimated as ∼72.6 J s−1 (∼2.51 mm day−1) and that over tropical land is ∼73.7 J s−1 (∼2.55 mm day−1) for 30°N–30°S. It is shown that nondrizzle precipitation over tropical and subtropical oceans consists of two dominant modes of rainfall systems: deep systems and congestus. A rough estimate of the shallow-heating contribution against the total heating is about 46.7% for the average tropical oceans, which is substantially larger than the 23.7% over tropical land. Although cumulus congestus heating linearly correlates with SST, deep-mode heating is dynamically bounded by large-scale subsidence. It is notable that a substantial amount of rain, as large as 2.38 mm day−1 on average, is brought from congestus clouds under the large-scale subsiding circulation. It is also notable that, even in the region with SSTs warmer than 28°C, large-scale subsidence effectively suppresses the deep convection, with the remaining heating by congestus clouds. The results support that the entrainment of mid–lower-tropospheric dry air, which accompanies the large-scale subsidence, is the major factor suppressing the deep convection. Therefore, a representation of the realistic entrainment is very important for proper reproduction of precipitation distribution and the resultant large-scale circulation.

scholarly journals Submonthly Indian Ocean Cooling Events and Their Interaction with Large-Scale Conditions

2010 ◽  
Vol 23 (3) ◽  
pp. 700-716 ◽  
Author(s):  
Ian D. Lloyd ◽  
Gabriel A. Vecchi
Keyword(s):  
Indian Ocean ◽  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Low Frequency ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Indian Ocean ◽  
Global Climate Models ◽  
Standard Deviations ◽  
Frequency Changes

Abstract The Indian Ocean exhibits strong variability on a number of time scales, including prominent intraseasonal variations in both the atmosphere and ocean. Of particular interest is the south tropical Indian Ocean thermocline ridge, a region located between 12° and 5°S, which exhibits prominent variability in sea surface temperature (SST) due to dominant winds that raise the thermocline and shoal the mixed layer. In this paper, submonthly (less than 30 day) cooling events in the thermocline ridge region are diagnosed with observations and models, and are related to large-scale conditions in the Indo-Pacific region. Observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite were used to identify 16 cooling events in the period 1998–2007, which on average cannot be fully accounted for by air–sea enthalpy fluxes. Analysis of observations and a hierarchy of models, including two coupled global climate models (GFDL CM2.1 and GFDL CM2.4), indicates that ocean dynamical changes are important to the cooling events. For extreme cooling events (above 2.5 standard deviations), air–sea enthalpy fluxes account for approximately 50% of the SST signature, and oceanic processes cannot in general be neglected. For weaker cooling events (1.5–2.5 standard deviations), air–sea enthalpy fluxes account for a larger fraction of the SST signature. Furthermore, it is found that cooling events are preconditioned by large-scale, low-frequency changes in the coupled ocean–atmosphere system. When the thermocline is unusually shallow in the thermocline ridge region, cooling events are more likely to occur and are stronger; these large-scale conditions are more (less) likely during La Niña (El Niño/Indian Ocean dipole) events. Strong cooling events are associated with changes in atmospheric convection, which resemble the Madden–Julian oscillation, in both observations and the models.

scholarly journals Using Deep Learning and Machine Learning Methods to Diagnose Hailstorms in Large-Scale Thermodynamic Environments

2020 ◽  
Vol 12 (24) ◽  
pp. 10499
Author(s):  
Farha Pulukool ◽  
Longzhuang Li ◽  
Chuntao Liu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Tropical Rainfall Measuring Mission ◽  
Global Scale ◽  
Weather Forecasts ◽  
One Year

Hailstorms have caused damages in billions of dollars to industrial, electronic, and mechanical properties such as automobiles, buildings, roads, and aircrafts, as well as life threats to crop and cattle populations, due to their hazardous nature. Hence, the relevance of predicting hailstorms in the future has significant scientific, economic, and societal benefits. However, climate models do not have adequate resolutions to explicitly resolve these subscale phenomena. One solution is to estimate the probability of these storms by using large-scale atmospheric thermodynamic environment variables from climate model outputs, but the existing methods only carried out experiments on small datasets limited to a region, country, or location and a large number of input features. Using one year of Tropical Rainfall Measuring Mission (TRMM) observations and European Center for Medium-Range Weather Forecasts (ECMWF) Re-Analysis Interim (ERA-Interim) reanalysis on a global scale, this paper develops two deep-learning-based models (an autoencoder and convolutional neural network (CNN)) as well as a machine learning approach (random forest) for hailstorm prediction by using only four attributes—convective potential energy, convective inhibition, 1–3 km wind shear, and warm cloud depth. In the experiments, the random forest approach produces the best hailstorm prediction performance compared to the other two methods.

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