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Note that here the presented levels are sigma levels and geopotential height is a modified geopotential variable that includes surface pressure.

As a result, circulation follows the terrain throughout the model depth. Geopotential height is visualised by five contours between the maximal and minimal value in each panel.

Figure 3 shows that, on average, the equatorial Kelvin wave signal is dominated by the zonal wavenumber 1 and has the largest amplitude over the Indian ocean in July.

The propagation of Kelvin waves in the model forecasts is illustrated in Figure 4. Here, we put together results of the modal decomposition every 12 hours and show both zonal wind and temperature perturbations, computed from geopotential perturbations using the hydrostatic relationship.

Although the decomposition is performed independently for each time step, when the outputs for successive times are put together, they naturally connect and show propagation properties known from linear theory and studies based on frequency filtering.

This is a strong justification for the assumptions made for the derivation of normal modes used for the decomposition.

It should be noted that the presented wave properties are the result of a summation over 70 vertical modes. Figure 3 Kelvin wave winds arrows and geopotential height perturbations shading in January at a model level 68 approx.

Averaging is performed for analyses from 00 UTC. Figure 4 The evolution of Kelvin waves in the day forecast started on 20 July , 00 UTC, showing zonal wind speed perturbations shading and Kelvin wave temperature perturbations isolines every 2 K , with positive perturbations drawn in solid lines and negative perturbations in dashed lines, for a level 29 approx.

The verification can also be performed in physical space for any mode of interest. The growth of RMSEs in Figure 5 is associated with the variability of the tropical stratosphere, which is driven by vertically propagating equatorial waves associated with convection.

In July, convection is most intense over the maritime continent and the errors in stratospheric circulation first develop here.

By contrast, the RMSEs of the stratospheric unbalanced zonal wind component propagate eastward in forecasts in both July Figure 5b and January not shown , but the error amplitudes are greater and develop earlier in the forecasts in July.

The location of the maximal stratospheric RMSE is not the same in January and July as the most intense convection, which generates vertically propagating IG waves, moves along the equator not shown.

The fact that RMSEs for balanced flow along the equator in Figure 5a,b appear smoother than RMSEs for unbalanced flow can possibly be explained by the dynamical properties of IG waves and their generation by physical processes.

The growth of zonally averaged tropical forecast errors in the zonal wind component is largest in the balanced component in the upper troposphere.

In the day range, the balanced error at level hPa is nearly twice as large as the error in the unbalanced component Figure 5c,d.

The two components initially have similar amplitudes, which indicates that the analysis errors relative to the variability of the wind is higher in the unbalanced component compared to the balanced component, or possibly that the error growth is much higher during the first forecast day.

In the stratosphere level 50 the unbalanced component of error dominates in both January and July but the error growth is greatest in July, when convection is stronger.

In the next section we are going to discuss short-range errors by presenting average analysis increments. Another useful error decomposition is presented in Figure 6, which shows zonal wind analysis increments in autumn as the mean absolute differences between the analysis and the first-guess forecast.

The average increments are small if the short-range forecast first-guess agrees with the available observations.

The increments are also small if there was no significant error growth in the short-range forecast. In general, one expects the increments to be larger in dynamically active regions with faster intrinsic error growth e.

To understand the nature of errors, the decomposition of the increments into balanced and unbalanced components could be valuable.

Figure 6 Mean analysis increments of zonal wind from September to November showing a total increments at model level approx.

Increments are computed as mean absolute differences between hour forecasts and analyses valid at 18 UTC. Global analysis increments for all modes, split into balanced and unbalanced modes, are shown in Figure 6 at two model levels.

The level close to hPa represents the flow in the upper troposphere in the tropics and in the lower stratosphere in the mid-latitudes.

The increments are largest at the hPa level in the tropics. Lower in the troposphere, the increments are distributed more evenly and appear smoother, especially for the balanced component in the mid-latitudes.

Note also that smaller increments at the hPa level in the extra-tropics are above the tropopause, where variability is significantly smaller.

In the tropics, a larger part of increments is associated with unbalanced modes than with balanced modes at both levels.

The largest increments are found over tropical Africa. A further diagnostic into various modes reveals that some of the increments over Africa are associated with the Kelvin modes not shown.

Furthermore, these increments, which are believed to be connected to convection over Africa, are strongest during daytime 18 UTC analysis.

There are also big increments in unbalanced flow over eastern Africa, which is related to very localised and unrealistic convection in the model over Ethiopia.

This feature has been improved in the new IFS model cycle 41r2. Overall, Figure 6 suggests that data assimilation is most difficult in the tropics, where forecast errors grow fastest and where a lack of direct wind observations makes it difficult to constrain circulation in the analyses.

Figure 6 also shows that in high-resolution forecasts a significant part of tropospheric analysis increments projects onto unbalanced modes in the extra-tropics too.

We have presented a new diagnostic technique that can usefully be applied to ECMWF forecasts in the tropics and that complements other methods to validate model performance.

Based on a decomposition into balanced and unbalanced IG modes, the technique enables balanced flow features, such as individual equatorial Rossby waves, and unbalanced waves, such as Kelvin waves, to be evaluated separately.

In the belly of the whale, Mike encounters his uncle's assistant, who confirms that Dr. Jones was abducted by aliens, and out of fear, he did not give Mike all possible help when they met earlier on C-island.

After they escape the whale, the assistant gives Mike a special code, which enables Nav-Com to track Dr.

Jones's location. Mike follows the signal to the lost ruins which includes the melted wreckage of an alien escape pod.

Shortly afterward, Mike finds his uncle. Jones explains that he discovered the escape pod some time ago, and says it came from a far-away planet called Argonia.

This escape pod contained three magic cubes, which are now in the hands of the evil alien's leader Zoda.

Infiltrating their spaceship, Mike recovers the three cubes and confronts Zoda. Mike defeats Zoda and then escapes as the spaceship self-destructs.

After Mike returns to C-Island, the cubes are placed together and a small group of children appear. The leader of the children, Mica, explains that they are the last of the Argonians their home planet having been destroyed and that her father King Hirocon sent them to Earth to live in peace.

The village chief invites the children to live with them in Coralcola, to which they accept. StarTropics is played from a 2D, top-down perspective, similar to many other role-playing games of that era.

The game is divided into several chapters; in each chapter, players take control of the protagonist, "Mike," exploring various settlements and other areas of interest and interacting with non-player characters in order to obtain more information about the surroundings.

The player is then usually tasked with locating the source of some local calamity or disturbance.

When the player enters a more dangerous locale , the game switches mechanics, bringing the view closer in and introducing various obstacles and adversaries that the player must either navigate or destroy.

A yo-yo serves as Mike's primary weapon renamed "star" in the Virtual Console release [7]. As the player progresses, other weapons and tools are made available that will aid in Mike's journey, including several items influenced by American baseball.

The game was also packaged with a physical letter, which set up the story and was used within the game's plot. Tropical jungles and rainforests have much more humid and hotter weather than colder and drier temperaments of the Northern Hemisphere.

This theme led to some scholars to suggest that humid hot climates correlate to human populations lacking control over nature e.

From Wikipedia, the free encyclopedia. Region of the Earth surrounding the Equator. For other uses, see Tropic disambiguation and Tropical disambiguation.

Main articles: Tropical climate and Wet season. Geography portal. God Plays Dice. Retrieved National Geographic Encyclopedia.

National Geographic Society. Science Magazine. Rainy season. Retrieved on Benders-Hyde World Climates.

Blue Planet Biomes. Akintola Rainstorm characteristics affecting water availability for agriculture. Journal of Tropical Geography.

Tropics Mode Video

Tropical plants and animals are those species native to the tropics. Tropical ecosystems may consist of tropical rainforests , seasonal tropical forests , dry often deciduous forests , spiny forests, desert and other habitat types.

There are often significant areas of biodiversity , and species endemism present, particularly in rainforests and seasonal forests.

Some examples of important biodiversity and high endemism ecosystems are El Yunque National Forest in Puerto Rico , Costa Rican and Nicaraguan rainforests, Amazon Rainforest territories of several South American countries, Madagascar dry deciduous forests , the Waterberg Biosphere of South Africa , and eastern Madagascar rainforests.

Often the soils of tropical forests are low in nutrient content, making them quite vulnerable to slash-and-burn deforestation techniques, which are sometimes an element of shifting cultivation agricultural systems.

Together, they are sometimes referred to as the Pantropic. The system of biogeographic realms differs somewhat; the Neotropical realm includes both the Neotropics and temperate South America, and the Paleotropics correspond to the Afrotropical , Indomalayan , Oceanian , and tropical Australasian realms.

Tropicality refers to the image that people outside the tropics have of the region, ranging from critical to verging on fetishism.

The idea of tropicality gained renewed interest in geographical discourse when French geographer Pierre Gourou published Les Pays Tropicaux The Tropical World in English , in the late s.

Tropicality encompassed two images. One, is that the tropics represent a 'Garden of Eden', a heaven on Earth, a land of rich biodiversity - aka a tropical paradise.

The latter view was often discussed in old Western literature more so than the first. Western scholars tried to theorize reasons about why tropical areas were relatively more inhospitable to human civilisations then those existing in colder regions of the Northern Hemisphere.

A popular explanation focused on the differences in climate. Tropical jungles and rainforests have much more humid and hotter weather than colder and drier temperaments of the Northern Hemisphere.

This theme led to some scholars to suggest that humid hot climates correlate to human populations lacking control over nature e.

From Wikipedia, the free encyclopedia. Region of the Earth surrounding the Equator. For other uses, see Tropic disambiguation and Tropical disambiguation.

Main articles: Tropical climate and Wet season. Geography portal. God Plays Dice. Retrieved National Geographic Encyclopedia.

National Geographic Society. Science Magazine. Rainy season. Retrieved on Benders-Hyde World Climates. The 3D NMFs represent surface pressure, temperature and wind fields simultaneously.

The method relies on a representation of the global baroclinic atmosphere in terms of M global shallow-water systems, each characterised by its own fluid depth for horizontal flow, known as the equivalent depth.

The projection procedure consists of a vertical projection followed by the horizontal step. The basis functions for the horizontal projection are the Hough harmonics.

For every given vertical mode, the Hough harmonics are characterised by the zonal wavenumber and meridional mode.

Recently available software provides outputs also in NetCDF format. The outputs of the modal decomposition are the complex Hough expansion coefficients, given as a function of vertical mode m , meridional mode n and the zonal wavenumber k.

For every meridional mode, there are three solutions: a balanced mode, which obeys the dispersion relationship for Rossby waves, an eastward-propagating inertia-gravity mode and a westward-propagating inertia-gravity mode, denoted EIG and WIG, respectively.

The MODES software decomposes global dynamical fields into balanced and unbalanced eastward- and westward-propagating IG flow at different vertical and horizontal scales.

This is achieved by representing the fields as a sum of oscillations, called Hough harmonics, with different vertical modes, meridional modes and zonal wavenumbers.

The method is described in greater detail in Box A. Since October , ECMWF's operational day forecast has been analysed using this method with a time step of 12 hours.

However, not all vertical modes are included as the numerical solution of the vertical structure equation limits the usefulness of vertical modes with high vertical mode index.

One of the MODES outputs is the distribution of total atmospheric energy in balanced and unbalanced components as a function of zonal wavenumber.

Figure 1 shows the January average energy distribution for 00 UTC analyses and the corresponding day forecasts.

The energy spectra are shown up to the zonal wavenumber , which corresponds to a grid spacing of about km at the equator and about 80 km in the mid-latitudes.

As the spectral energy distribution presented in Figure 1 is different from commonly used energy spectra in the IFS, it may be useful to discuss the differences.

Figure 1 Atmospheric energy distribution in balanced and unbalanced flow as a function of the zonal wavenumber in January for 00 UTC analyses and day forecasts.

The energy is summed over all meridional and vertical scales. The basis functions used to produce Figure 1 are the Hough harmonics, whereas global spectral models such as the IFS use spherical harmonics.

The latter are the eigensolution of the global barotropic vorticity equation whereas the former are the eigensolution of the global linearised shallow-water equations.

A scale-dependent distribution of atmospheric energy is readily produced from both types of harmonic representation with an important difference: the spherical harmonics provide a kinetic energy spectrum at a given horizontal level as a function of the zonal or global wavenumber, whereas the Hough harmonics provide the spectrum of kinetic and available potential energy of horizontal motions associated with a prescribed equivalent depth i.

In other words, the spectra in Figure 1 include available potential energy and the whole model depth. Furthermore, the application of spherical harmonics allows the decomposition of kinetic energy into rotational and divergent components, whereas the Hough harmonics provide an energy decomposition into balanced or vorticity-dominated Rossby and unbalanced or IG, mainly divergent components.

The divergent energy spectra are often regarded as synonymous with IG spectra in mid-latitude mesoscale conditions. On large scales and in the tropics, such an assumption is not valid.

For example, the equatorial Kelvin wave is a half-rotational and half-divergent mode and as such difficult to extract from IFS data.

By contrast, the computation of rotational and divergent energy for the wavenumber k from spherical harmonics involves derivatives and thus depends on velocities in neighbouring wavenumbers.

For example, the planetary scales were significantly more energetic in January than in July not shown. Comparing these with the analyses suggests that HRES tended to somewhat underpredict the variability at most scales, especially at zonal wavenumber 2.

The modal decomposition can be used to filter any mode and spatial scale back to physical space.

For example, Figure 2 presents balanced and unbalanced flow in January and July for a level at the tropical tropopause.

In this region, the unbalanced winds were of the same direction, whereas over the central Pacific they were stronger and with the opposite sign compared to the balanced flow Figure 2b,c.

In July, the unbalanced winds were strongest over the Indian Ocean region in relation to the summer monsoon. Overall, Figure 2 shows that a significant component of large-scale tropical circulation is unbalanced in both months.

In the lower tropical troposphere, unbalanced winds tend to be stronger than balanced winds, especially in the cross-equatorial component not shown.

Figure 2 Average horizontal winds and geopotential height shading at model level 60 approximately hPa in the tropics in January showing a total average flow, b balanced average flow and c unbalanced average flow; and in July showing d total average flow, e balanced average flow and f unbalanced average flow.

Averaging is performed for analyses at 00 UTC. Note that here the presented levels are sigma levels and geopotential height is a modified geopotential variable that includes surface pressure.

As a result, circulation follows the terrain throughout the model depth. Geopotential height is visualised by five contours between the maximal and minimal value in each panel.

Figure 3 shows that, on average, the equatorial Kelvin wave signal is dominated by the zonal wavenumber 1 and has the largest amplitude over the Indian ocean in July.

The propagation of Kelvin waves in the model forecasts is illustrated in Figure 4. Here, we put together results of the modal decomposition every 12 hours and show both zonal wind and temperature perturbations, computed from geopotential perturbations using the hydrostatic relationship.

Although the decomposition is performed independently for each time step, when the outputs for successive times are put together, they naturally connect and show propagation properties known from linear theory and studies based on frequency filtering.

This is a strong justification for the assumptions made for the derivation of normal modes used for the decomposition. It should be noted that the presented wave properties are the result of a summation over 70 vertical modes.

Figure 3 Kelvin wave winds arrows and geopotential height perturbations shading in January at a model level 68 approx. Averaging is performed for analyses from 00 UTC.

Figure 4 The evolution of Kelvin waves in the day forecast started on 20 July , 00 UTC, showing zonal wind speed perturbations shading and Kelvin wave temperature perturbations isolines every 2 K , with positive perturbations drawn in solid lines and negative perturbations in dashed lines, for a level 29 approx.

The verification can also be performed in physical space for any mode of interest. The growth of RMSEs in Figure 5 is associated with the variability of the tropical stratosphere, which is driven by vertically propagating equatorial waves associated with convection.

In July, convection is most intense over the maritime continent and the errors in stratospheric circulation first develop here.

By contrast, the RMSEs of the stratospheric unbalanced zonal wind component propagate eastward in forecasts in both July Figure 5b and January not shown , but the error amplitudes are greater and develop earlier in the forecasts in July.

The location of the maximal stratospheric RMSE is not the same in January and July as the most intense convection, which generates vertically propagating IG waves, moves along the equator not shown.

The fact that RMSEs for balanced flow along the equator in Figure 5a,b appear smoother than RMSEs for unbalanced flow can possibly be explained by the dynamical properties of IG waves and their generation by physical processes.

The growth of zonally averaged tropical forecast errors in the zonal wind component is largest in the balanced component in the upper troposphere.

In the day range, the balanced error at level hPa is nearly twice as large as the error in the unbalanced component Figure 5c,d.

The two components initially have similar amplitudes, which indicates that the analysis errors relative to the variability of the wind is higher in the unbalanced component compared to the balanced component, or possibly that the error growth is much higher during the first forecast day.

In the stratosphere level 50 the unbalanced component of error dominates in both January and July but the error growth is greatest in July, when convection is stronger.

In the next section we are going to discuss short-range errors by presenting average analysis increments. Another useful error decomposition is presented in Figure 6, which shows zonal wind analysis increments in autumn as the mean absolute differences between the analysis and the first-guess forecast.

The average increments are small if the short-range forecast first-guess agrees with the available observations. The increments are also small if there was no significant error growth in the short-range forecast.

In general, one expects the increments to be larger in dynamically active regions with faster intrinsic error growth e.

To understand the nature of errors, the decomposition of the increments into balanced and unbalanced components could be valuable.

Figure 6 Mean analysis increments of zonal wind from September to November showing a total increments at model level approx.

Tropics Mode - Öffnungs­zeiten

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